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  • English  (84)
  • 2015-2019  (84)
  • Birmingham, UK : Packt Publishing  (80)
  • Cham : Springer  (4)
  • Data mining  (84)
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  • English  (84)
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  • 1
    Online Resource
    Online Resource
    Kibana 7 quick start guide : visualize your Elasticsearch data with ease (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789808490 , 1789808499
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: A quick start guide to visualize your Elasticsearch data Key Features Your hands-on guide to visualizing the Elasticsearch data as well as navigating the Elastic stack Work with different Kibana plugins and create effective machine learning jobs using Kibana Build effective dashboards and reports without any hassle Book Description The Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective. This book endeavors to explain all the important aspects of Kibana, which is essential for utilizing its full potential. This book covers the core concepts of Kibana, with chapters set out in a coherent manner so that readers can advance their learning in a step-by-step manner. The focus is on a practical approach, thereby enabling the reader to apply those examples in real time for a better understanding of the concepts and to provide them with the correct skills in relation to the tool. With its succinct explanations, it is quite easy for a reader to use this book as a reference guide for learning basic to advanced implementations of Kibana. The practical examples, such as the creation of Kibana dashboards from CSV data, application RDBMS data, system metrics data, log file data, APM agents, and search results, can provide readers with a number of different drop-off points from where they can fetch any type of data into Kibana for the purpose of analysis or dashboarding. What you will learn Explore how Logstash is configured to fetch CSV data Understand how to create index patterns in Kibana Become familiar with how to apply filters on data Discover how to create ML jobs Explore how to analyze APM data from APM agents Get to grips with how to save, share, inspect, and edit visualizations Understand how to find an anomaly in data Who this book is for Kibana 7 Quick Start Guide is for developers new to Kibana who want to learn the fundamentals of using the tool for visualization, as well as existing Elastic developers.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 26, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789804034/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Data wrangling with Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789804249 , 1789804248
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Data mining ; Electronic data processing ; Information visualization ; Electronic books ; local ; Electronic books
    Abstract: Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features Focus on the basics of data wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data generation and data integrity checks Book Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learn Use and manipulate complex and simple data structures Harness the full potential of DataFrames and numpy.array at run time Perform web scraping with BeautifulSoup4 and html5lib Execute advanced string search and manipulation with RegEX Handle outliers and perform data imputation with Pandas Use descriptive statistics and plotting techniques Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
    Note: Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Go web scraping quick start guide : implement the power of Go to scrape and crawl data from the web (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789612943 , 1789612942
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Go (Computer program language) ; Data mining ; Internet searching ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Learn how some Go-specific language features help to simplify building web scrapers along with common pitfalls and best practices regarding web scraping. Key Features Use Go libraries like Goquery and Colly to scrape the web Common pitfalls and best practices to effectively scrape and crawl Learn how to scrape using the Go concurrency model Book Description Web scraping is the process of extracting information from the web using various tools that perform scraping and crawling. Go is emerging as the language of choice for scraping using a variety of libraries. This book will quickly explain to you, how to scrape data data from various websites using Go libraries such as Colly and Goquery. The book starts with an introduction to the use cases of building a web scraper and the main features of the Go programming language, along with setting up a Go environment. It then moves on to HTTP requests and responses and talks about how Go handles them. You will also learn about a number of basic web scraping etiquettes. You will be taught how to navigate through a website, using a breadth-first and then a depth-first search, as well as find and follow links. You will get to know about the ways to track history in order to avoid loops and to protect your web scraper using proxies. Finally the book will cover the Go concurrency model, and how to run scrapers in parallel, along with large-scale distributed web scraping. What you will learn Implement Cache-Control to avoid unnecessary network calls Coordinate concurrent scrapers Design a custom, larger-scale scraping system Scrape basic HTML pages with Colly and JavaScript pages with chromedp Discover how to search using the "strings" and "regexp" packages Set up a Go development environment Retrieve information from an HTML document Protect your web scraper from being blocked by using proxies Control web browsers to scrape JavaScript sites Who this book is for Data scientists, and web developers with a basic knowledge of Golang wanting to collect web data and analyze them for effective reporting and visualization.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789615708/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Hands-on business intelligence with Qlik Sense : implement self-service data analytics with insights and guidance from Qlik Sense experts (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789800593 , 1789800595
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Management information systems ; Business intelligence ; Computer programs ; Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Create dynamic dashboards to bring interactive data visualization to your enterprise using Qlik Sense Key Features Implement various Qlik Sense features to create interactive dashboards Analyze data easily and make business decisions faster using Qlik Sense Perform self-service data analytics and geospatial analytics using an example-based approach Book Description Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions. Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense's features. You will learn advanced modeling techniques and learn how to analyze the data loaded using a variety of visualization objects. You'll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you'll explore the stories feature to create data-driven presentations and update an existing story. This book will guide you through the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you'll learn about the self-service analytics features and perform data forecasting using advanced analytics. Lastly, you'll deploy Qlik Sense apps for mobile and tablet. By the end of this book, you will be well-equipped to run successful business intelligence applications using Qlik Sense's functionality, data modeling techniques, and visualization best practices. What you will learn Discover how to load, reshape, and model data for analysis Apply data visualization practices to create stunning dashboards Make use of Python and R for advanced analytics Perform geo-analysis to create visualizations using native objects Learn how to work with AGGR and data stories Who this book is for If you're a data analyst, BI developer, or interested in business intelligence and want to gain practical experience of working on Qlik Sense, this book is for you. You'll also find it useful if you want to explore Qlik Sense's next-generation applications for self-service business intelligence. No prior experience of working with Qlik Sense is required.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789800944/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Applied unsupervised learning with R (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789951462 , 1789951461
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Machine learning ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key Features Build state-of-the-art algorithms that can solve your business' problems Learn how to find hidden patterns in your data Revise key concepts with hands-on exercises using real-world datasets Book Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learn Implement clustering methods such as k-means, agglomerative, and divisive Write code in R to analyze market segmentation and consumer behavior Estimate distribution and probabilities of different outcomes Implement dimension reduction using principal component analysis Apply anomaly detection methods to identify fraud Design algorithms with R and learn how to edit or improve code Who this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including ex...
    Note: Includes bibliographical references. - Description based on online resource; title from copyright page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789956399/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Machine Learning Techniques for Online Social Networks (2018)
    Cham : Springer
    Details
    ISBN: 9783319899329
    Language: English
    Pages: Online-Ressource (VIII, 236 p. 102 illus., 85 illus. in color, online resource)
    Series Statement: Lecture Notes in Social Networks
    Series Statement: SpringerLink
    Series Statement: Bücher
    Parallel Title: Erscheint auch als
    DDC: 302.23/1
    Keywords: Social sciences ; Social media ; Data mining ; Artificial intelligence ; Social Sciences ; Social sciences ; Social media ; Data mining ; Artificial intelligence
    Abstract: The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields
    Abstract: Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity -- Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs -- Chapter3. A Framework for OSN Performance Evaluation Studies -- Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks -- Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content -- Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning -- Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability -- Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements -- Chapter9. Dynamics of large scale networks following a merger -- Chapter10. Cloud Assisted Personal Online Social Network -- Chapter11. Text-Based Analysis of Emotion by Considering Tweets
    Note: Includes bibliographical references
    DOI: 10.1007/978-3-319-89932-9
    URL: Volltext
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    URL: Volltext
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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    Online Resource
    BSZ
    GBV
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    Numerical computing with Python : harness the power of Python to analyze and find hidden patterns in the data (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789957228 , 1789957222
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Data mining ; Information visualization ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key Features Use the power of Pandas and Matplotlib to easily solve data mining issues Understand the basics of statistics to build powerful predictive data models Grasp data mining concepts with helpful use-cases and examples Book Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: Statistics for Machine Learning by Pratap Dangeti Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim Pandas Cookbook by Theodore Petrou What you will learn Understand the statistical fundamentals to build data models Split data into independent groups Apply aggregations and transformations to each group Create impressive data visualizations Prepare your data and design models Clean up data to ease data analysis and visualization Create insightful visualizations with Matplotlib and Seaborn Customize the model to suit your own predictive goals Who this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 20, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789953633/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    Splunk 7 essentials : demystify machine data by leveraging datasets, building reports, and sharing powerful insights (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788830126 , 1788830121
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Electronic books ; Electronic books ; local
    Abstract: Transform machine data into powerful analytical intelligence using Splunk About This Book Analyze and visualize machine data to step into the world of Splunk! Leverage the exceptional analysis and visualization capabilities to make informed decisions for your business This easy-to-follow, practical book can be used by anyone - even if you have never managed data before Who This Book Is For This book is for the beginners who want to get well versed in the services offered by Splunk 7. If you want to be a data/business analyst or want to be a system administrator, this book is what you want. No prior knowledge of Splunk is required. What You Will Learn Install and configure Splunk for personal use Store event data in Splunk indexes, classify events into sources, and add data fields Learn essential Splunk Search Processing Language commands and best practices Create powerful real-time or user-input dashboards Be proactive by implementing alerts and scheduled reports Tips from the Fez: best practices using Splunk features and add-ons Understand security and deployment considerations for taking Splunk to an organizational level In Detail Splunk is a search, reporting, and analytics software platform for machine data, which has an ever-growing market adoption rate. More organizations than ever are adopting Splunk to make informed decisions in areas such as IT operations, information security, and the Internet of Things. The first two chapters of the book will get you started with a simple Splunk installation and set up of a sample machine data generator, called Eventgen. After this, you will learn to create various reports, dashboards, and alerts. You will also explore Splunk's Pivot functionality to model data for business users. You will then have the opportunity to test-drive Splunk's powerful HTTP Event Collector. After covering the core Splunk functionality, you'll be provided with some real-world best practices for using Splunk, and information on how to build upon what you've learned in this book. Throughout the book, there will be additional comments and best practice recommendations from a member of the SplunkTrust Community, called "Tips from the Fez". Style and approach This fast-paced, example-rich guide will help you analyze and visualize machine data with Splunk through simple, practical instructions and recommendations. Downloading the example code for this book You can download the example code files for all Packt books you have purc...
    Note: Description based on online resource; title from title page (Safari, viewed April 30, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788839112/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    Hands-on data science with R : techniques to perform data manipulation and mining to build smart analytical models using R (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789135831 , 1789135834
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Mathematical statistics ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789139402/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 10
    Online Resource
    Online Resource
    Hands-on predictive analytics with Python : master the complete predictive analytics process, from problem definition to model deployment (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789134544 , 1789134544
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Decision making ; Data processing ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Step-by-step guide to build high performing predictive applications Key Features Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Book Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learn Get to grips with the main concepts and principles of predictive analytics Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Who this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this excitin...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 26, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789138719/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 11
    Online Resource
    Online Resource
    R web scraping quick start guide : techniques and tools to crawl and scrape data from websites (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788992633 , 1788992636
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Data mining ; Information retrieval ; Electronic books ; Electronic books ; local
    Abstract: Web Scraping techniques are getting more popular, since data is as valuable as oil in 21st century. Through this book get some key knowledge about using XPath, regEX; web scraping libraries for R like rvest and RSelenium technologies. Key Features Techniques, tools and frameworks for web scraping with R Scrape data effortlessly from a variety of websites Learn how to selectively choose the data to scrape, and build your dataset Book Description Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming. You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R. What you will learn Write and create regEX rules Write XPath rules to query your data Learn how web scraping methods work Use rvest to crawl web pages Store data retrieved from the web Learn the key uses of Rselenium to scrape data Who this book is for This book is for R programmers who want to get started quickly with web scraping, as well as data analysts who want to learn scraping using R. Basic knowledge of R is all you need to get started with this book.
    Note: Description based on online resource; title from title page (viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789138733/?ar
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    MPI Ethno. Forsch.
  • 12
    Online Resource
    Online Resource
    Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789959918 , 1789959918
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Big data ; Machine learning ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.
    Note: Description based on online resource; title from title page (Safari, viewed March 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789959208/?ar
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    MPI Ethno. Forsch.
  • 13
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    Beginning data analysis with Python and Jupyter : use powerful industry-standard tools to unlock new, actionable insight from your existing data (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789534658 , 1789534658
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Information visualization ; Electronic data processing ; Data mining
    Note: Description based on online resource; title from cover (Safari, viewed July 18, 2018). - Includes index
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781789532029/?ar
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  • 14
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    R deep learning projects : master the techniques to design and develop neural network models in R (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788474559 , 1788474554
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Machine learning ; R (Computer program language) ; Data mining
    Note: Description based on online resource; title from title page (viewed March 14, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788478403/?ar
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  • 15
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    Learning Jupyter 5 : explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789137446 , 1789137446
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Science ; Data processing ; Data mining ; Information visualization ; Python (Computer program language) ; Java (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter Key Features Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles Leverage big data tools and datasets with different Python packages Explore multiple-user Jupyter Notebook servers Book Description The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you've explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization. What you will learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you on the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter for dealing with large datasets using Spark Who this book is for Learning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If ...
    Note: Previous edition published: 2016. - Description based on online resource; title from title page (Safari, viewed October 5, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789137408/?ar
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  • 16
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    Hands-on big data modeling : effective database design techniques for data architects and business intelligence professionals (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788626088 , 1788626087
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Database design ; Big data ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Solve all big data problems by learning how to create efficient data models Key Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you'll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you'll work with structured and semi-structured data with the help of real-life examples. Once you've got to grips with the basics, you'll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You'll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you'll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788620901/?ar
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  • 17
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    Mastering Qlik Sense : expert techniques on self-service data analytics to create enterprise ready business intelligence solutions (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781782174875 , 1782174877
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Management information systems ; Business intelligence ; Computer programs ; Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Master the capabilities of Qlik Sense to design and deploy solutions that address all the Business Intelligence needs of your organization About This Book Create compelling dashboards and visualizations with your data by leveraging Qlik Sense's self-service model Perform data loading and model efficient solutions with faster performance and better governance Master Qlik Sense's APIs and develop powerful mashups and fantastic extensions for visualizations and other components that run across all platforms Who This Book Is For This book is for Business Intelligence professionals and Data Analysts who want to become experts in using Qlik Sense. If you have extensively used QlikView in the past and are looking to transition to Qlik Sense, this book will also help you. A fundamental understanding of how Qlik Sense works and its features is all you need to get started with this book. What You Will Learn Understand the importance of self-service analytics and the IKEA-effect Explore all the available data modeling techniques and create efficient and optimized data models Master security rules and translate permission requirements into security rule logic Familiarize yourself with different types of Master Key Item(MKI) and know how and when to use MKI. Script and write sophisticated ETL code within Qlik Sense to facilitate all data modeling and data loading techniques Get an extensive overview of which APIs are available in Qlik Sense and how to take advantage of a technology with an API Develop basic mashup HTML pages and deploy successful mashup projects In Detail Qlik Sense is a powerful, self-servicing Business Intelligence tool for data discovery, analytics and visualization. It allows you to create personalized Business Intelligence solutions from raw data and get actionable insights from it. This book is your one-stop guide to mastering Qlik Sense, catering to all your organizational BI needs. You'll see how you can seamlessly navigate through tons of data from multiple sources and take advantage of the various APIs available in Qlik and its components for guided analytics. You'll also learn how to embed visualizations into your existing BI solutions and extend the capabilities of Qlik Sense to create new visualizations and dashboards that work across all platforms. We also cover other advanced concepts such as porting your Qlik View applications to Qlik Sense,and working with Qlik Cloud. Finally, you'll implement enterprise-wide security and ...
    Note: Description based on online resource; title from title page (viewed March 30, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781783554027/?ar
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  • 18
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    Pentaho data integration quick start guide : create ETL processes using Pentaho (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342796 , 1789342791
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data integration (Computer science) ; Data mining ; Decision support systems ; Database management ; Electronic books ; Electronic books ; local
    Abstract: Get productive quickly with Pentaho Data Integration Key Features Take away the pain of starting with a complex and powerful system Simplify your data transformation and integration work Explore, transform, and validate your data with Pentaho Data Integration Book Description Pentaho Data Integration(PDI) is an intuitive and graphical environment packed with drag and drop design and powerful Extract-Transform-Load (ETL) capabilities. Given its power and flexibility, initial attempts to use the Pentaho Data Integration tool can be difficult or confusing. This book is the ideal solution. This book reduces your learning curve with PDI. It provides the guidance needed to make you productive, covering the main features of Pentaho Data Integration. It demonstrates the interactive features of the graphical designer, and takes you through the main ETL capabilities that the tool offers. By the end of the book, you will be able to use PDI for extracting, transforming, and loading the types of data you encounter on a daily basis. What you will learn Design, preview and run transformations in Spoon Run transformations using the Pan utility Understand how to obtain data from different types of files Connect to a database and explore it using the database explorer Understand how to transform data in a variety of ways Understand how to insert data into database tables Design and run jobs for sequencing tasks and sending emails Combine the execution of jobs and transformations Who this book is for This book is for software developers, business intelligence analysts, and others involved or interested in developing ETL solutions, or more generally, doing any kind of data manipulation.
    Note: Description based on online resource; title from title page (Safari, viewed October 1, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789343328/?ar
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  • 19
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    TensorFlow : powerful predictive analytics with TensorFlow : predict valuable insights of your data with TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789130423 , 1789130425
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Data mining ; Big data ; Decision making Data processing ; Application software Development ; Machine learning
    Note: Description based on online resource; title from cover (Safari, viewed April 3, 2018). - "Rapid learning solution."
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781789136913/?ar
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  • 20
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    Implementing Splunk 7 : effective operational intelligence to transform machine-generated data into valuable business insight (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788830508 , 1788830504
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition
    Parallel Title: Erscheint auch als
    Keywords: Big data Data processing ; Data mining ; Automatic data collection systems
    Note: Description based on online resource; title from title page (Safari, viewed May 3, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788836289/?ar
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  • 21
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    Splunk operational intelligence cookbook : over 80 recipes for transforming your data into business-critical insights using Splunk (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788833080 , 1788833082
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition
    Parallel Title: Erscheint auch als
    RVK:
    ST 530
    Keywords: Big data ; Data mining ; Automatic data collection systems
    Note: Description based on online resource; title from title page (Safari, viewed June 29, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788835237/?ar
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  • 22
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    Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788839747 , 1788839749
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 232
    Keywords: Electronic data processing ; Data mining ; Information visualization ; Python (Computer program language) ; R (Computer program language) ; Scala (Computer program language)
    Note: Description based on online resource; title from title page (Safari, viewed May 31, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: lizenzpflichtig
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  • 23
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    Social Network Based Big Data Analysis and Applications (2018)
    Cham : Springer
    Details
    ISBN: 9783319781969
    Language: English
    Pages: Online-Ressource (X, 249 p. 89 illus., 77 illus. in color, online resource)
    Series Statement: Lecture Notes in Social Networks
    Series Statement: SpringerLink
    Series Statement: Bücher
    Series Statement: Springer eBook Collection
    Series Statement: Social Sciences
    Parallel Title: Erscheint auch als Social network based big data analysis and applications
    Parallel Title: Printed edition
    DDC: 310
    Keywords: Social sciences ; Data mining ; Social Sciences
    Abstract: This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field
    Abstract: Chapter1. Twitter as a Source for Time and Domain Dependent Sentiment Lexicons -- Chapter2. Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook -- Chapter3. Extraction and Analysis of Dynamic Conversational Networks from TV Series -- Chapter4. Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia -- Chapter5. Timelines of Prostate Cancer Biomarkers -- Chapter6. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks -- Chapter7. Influence and Extension of the Spiral of Silence in Social Networks: A Data-driven Approach -- Chapter8. Prepaid or Postpaid? That is the question.\\ Novel Methods of Subscription Type Prediction in Mobile Phone Services -- Chapter9. Dynamic Pattern Detection for Big Data Stream Analytics -- Chapter10. Community-based Recommendation for Cold-Start Problem: A Case Study of Reciprocal Online Dating Recommendation -- Chapter11. Combining Feature Extraction and Clustering for Better Face Recognition
    DOI: 10.1007/978-3-319-78196-9
    URL: Volltext  (lizenzpflichtig)
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  • 24
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    Become a Python data analyst : perform exploratory data analysis and gain insight into scientific computing using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789534405 , 1789534402
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; Python (Computer program language) ; Quantitative research ; Science ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
    Note: Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789531701/?ar
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  • 25
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    Hands-on recommendation systems with Python : start building powerful and personalized, recommendation engines with Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788992534 , 1788992539
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Internet marketing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory - you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
    Note: Description based on online resource; title from title page (Safari, viewed August 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788993753/?ar
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  • 26
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    IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789346695 , 178934669X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Watson (Computer) ; Natural language processing (Computer science) ; Data mining ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Incorporate intelligence to your data-driven business insights and high accuracy business solutions Key Features Explore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learning Build projects to adopt IBM Watson across retail, banking, and healthcare Learn forecasting, anomaly detection, and pattern recognition with ML techniques Book Description IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson. The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations. By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience. What you will learn Build a smart dialog system with cognitive assistance solutions Design a text categorization model and perform sentiment analysis on social media datasets Develop a pattern recognition application and identify data irregularities smartly Analyze trip logs from a driving services company to determine profit Provide insights into an organization's supply chain data and processes Create personalized recommendations for retail chains and outlets Test forecasting effectiveness for better sales prediction strategies Who this book is for This book is for data scientists, AI engineers, NLP engineers, machine learning engineers, and data analysts who wish to build next-generation analytics applications. Basic familiarity with cognitive computing and sound knowledge of any programming language is all you need to understand the projects covered in this book.
    Note: Description based on online resource; title from title page (Safari, viewed December 3, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789343717/?ar
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  • 27
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    Splunk 7.x quick start guide : gain business data insights from operational intelligence (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789538021 , 1789538025
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data. Key Features Understand the various components of Splunk and how they work together to provide a powerful Big Data analytics solution. Collect and index data from a wide variety of common machine data sources Design searches, reports, and dashboard visualizations to provide business data insights Book Description Splunk is a leading platform and solution for collecting, searching, and extracting value from ever increasing amounts of big data - and big data is eating the world! This book covers all the crucial Splunk topics and gives you the information and examples to get the immediate job done. You will find enough insights to support further research and use Splunk to suit any business environment or situation. Splunk 7.x Quick Start Guide gives you a thorough understanding of how Splunk works. You will learn about all the critical tasks for architecting, implementing, administering, and utilizing Splunk Enterprise to collect, store, retrieve, format, analyze, and visualize machine data. You will find step-by-step examples based on real-world experience and practical use cases that are applicable to all Splunk environments. There is a careful balance between adequate coverage of all the critical topics with short but relevant deep-dives into the configuration options and steps to carry out the day-to-day tasks that matter. By the end of the book, you will be a confident and proficient Splunk architect and administrator. What you will learn Design and implement a complex Splunk Enterprise solution Configure your Splunk environment to get machine data in and indexed Build searches to get and format data for analysis and visualization Build reports, dashboards, and alerts to deliver critical insights Create knowledge objects to enhance the value of your data Install Splunk apps to provide focused views into key technologies Monitor, troubleshoot, and manage your Splunk environment Who this book is for This book is intended for experienced IT personnel who are just getting started working with Splunk and want to quickly become proficient with its usage. Data analysts who need to leverage Splunk to extract critical business insights from application logs and other machine data sources will also benefit from this book.
    Note: Description based on online resource; title from title page (Safari, viewed February 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789531091/?ar
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  • 28
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    Mastering Kibana 6.x : visualize your Elastic Stack data with histograms, maps, charts, and graphs (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788834032 , 1788834038
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Get to grips with Kibana and its advanced functions to create interactive visualizations and dashboards Key Features Explore visualizations and perform histograms, stats, and map analytics Unleash X-Pack and Timelion, and learn alerting, monitoring, and reporting features Manage dashboards with Beats and create machine learning jobs for faster analytics Book Description Kibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations. To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale. With Kibana's Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities. What you will learn Create unique dashboards with various intuitive data visualizations Visualize Timelion expressions with added histograms and stats analytics Integrate X-Pack with your Elastic Stack in simple steps Extract data from Elasticsearch for advanced analysis and anomaly detection using dashboards Build dashboards from web applications for application logs Create monitoring and alerting dashboards using Beats Who this book is for Mastering Kibana 6.x is for you if you are a big data engineer, DevOps engineer, or data scientist aspiring to go beyond data visualization at scale and gain maximum insights from their large datasets. Basic knowledge of Elasticstack will be an added advantage, although not mandatory.
    Note: Description based on online resource; title from title page (Safari, viewed August 29, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788831031/?ar
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  • 29
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    R : predictive analysis : master the art of predictive modeling (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788290852 , 1788290852
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Quantitative research ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naïve Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with ?messy data?, large data, communicating results, and facilitating reproducibility. You will then perform key predictive...
    Note: "Learning path.". - Includes bibliographical references and index. - Description based on online resource; title from cover (viewed April 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781788290371/?ar
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    Big data analytics with Java : big data analytics - massive, predictive, social and self-driving (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787282193 , 1787282198
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications a...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed August 18, 2017)
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    Java data analysis : data mining, big data analysis, NoSQL, and data visualization (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787286405 , 1787286401
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Data mining ; Information visualization ; Electronic data processing ; Relational databases ; Electronic books ; Electronic books ; local
    Abstract: Get the most out of the popular Java libraries and tools to perform efficient data analysis About This Book Get your basics right for data analysis with Java and make sense of your data through effective visualizations. Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning. This is your companion to understanding and implementing a solid data analysis solution using Java Who This Book Is For If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required. What You Will Learn Develop Java programs that analyze data sets of nearly any size, including text Implement important machine learning algorithms such as regression, classification, and clustering Interface with and apply standard open source Java libraries and APIs to analyze and visualize data Process data from both relational and non-relational databases and from time-series data Employ Java tools to visualize data in various forms Understand multimedia data analysis algorithms and implement them in Java. In Detail Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed October 31, 2017)
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    Learning data mining with Python : use Python to manipulate data and build predictive models (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787129566 , 178712956X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp...
    Note: Description based on online resource; title from title page (viewed May 16, 2017)
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    Scala and Spark for big data analytics : tame big data with Scala and Apache Spark! (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781783550500 , 1783550503
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Scala (Computer program language) ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics u...
    Note: Description based on online resource; title from title page (Safari, viewed August 9, 2017)
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    R : mining spatial, text, web, and social media data : create and customize data mioning algorithms : a course in three modules (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788290814 , 178829081X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining - social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands...
    Note: "Bater Makhabel, Pradeepta Mishra, Nathan Danneman, Richard Heimann."--Cover. - "Learning path"--Cover. - Includes bibliographical references and index. - Description based on online resource; title from title page (viewed July 6, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781788293747/?ar
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    Mastering Spark for data science : master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785888281 , 1785888285
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products About This Book Develop and apply advanced analytical techniques with Spark Learn how to tell a compelling story with data science using Spark's ecosystem Explore data at scale and work with cutting edge data science methods Who This Book Is For This book is for those who have beginner-level familiarity with the Spark architecture and data science applications, especially those who are looking for a challenge and want to learn cutting edge techniques. This book assumes working knowledge of data science, common machine learning methods, and popular data science tools, and assumes you have previously run proof of concept studies and built prototypes. What You Will Learn Learn the design patterns that integrate Spark into industrialized data science pipelines See how commercial data scientists design scalable code and reusable code for data science services Explore cutting edge data science methods so that you can study trends and causality Discover advanced programming techniques using RDD and the DataFrame and Dataset APIs Find out how Spark can be used as a universal ingestion engine tool and as a web scraper Practice the implementation of advanced topics in graph processing, such as community detection and contact chaining Get to know the best practices when performing Extended Exploratory Data Analysis, commonly used in commercial data science teams Study advanced Spark concepts, solution design patterns, and integration architectures Demonstrate powerful data science pipelines In Detail Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance ?solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLli...
    Note: Description based on online resource; title from cover (Safari, viewed April 19, 2017)
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    IBM SPSS Modeler essentials : effective techniques for building powerful data mining and predictive analytics solutions (2017)
    Birmingham, UK : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SPSS (Computer file) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up?and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions A quick, easy?to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface Import data into Modeler and learn how to properly declare metadata Obtain summary statistics and audit the quality of your data Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields Assess simple relationships using various statistical and graphing techniques Get an overview of the different types of models available in Modeler Build a decision tree model and assess its results Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn ?visual programming? style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This bo...
    Note: Description based on online resource; title from title page (viewed February 5, 2018)
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    Frank Kane's Taming big data with Apache Spark and Python : real-world examples to help you analyze large datasets with Apache Spark (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787288300 , 1787288307
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SPARK (Electronic resource) ; Data mining ; Python (Computer program language) ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain ? quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary i...
    Note: Description based on online resource; title from title page (Safari, viewed July 24, 2017)
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    Mastering Java for data science : building data science applications in Java (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785887390 , 1785887394
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrie...
    Note: Description based on online resource; title from title page (viewed May 15, 2017)
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    Python social media analytics : analyze and visualize data from Twitter, YouTube, GitHub, and more (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787126756 , 1787126757
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Data mining ; Social media ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python tools A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn Understand the basics of social media mining Use PyMongo to clean, store, and access data in MongoDB Understand user reactions and emotion detection on Facebook Perform Twitter sentiment analysis and entity recognition using Python Analyze video and campaign performance on YouTube Mine popular trends on GitHub and predict the next big technology Extract conversational topics on public internet forums Analyze user interests on Pinterest Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using th...
    Note: Description based on online resource; title from title page (Safari, viewed August 14, 2017)
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    Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787280229 , 1787280225
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Python (Computer program language) ; Machine learning ; Data mining ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehen...
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    Learning social media analytics with R : transform data from social media platforms into actionable insights (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787125469 , 1787125467
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; R (Computer program language) ; Social media ; Business ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical r...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed June 16, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787127524/?ar
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  • 42
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    Practical real-time data processing and analytics : distributed computing and event processing using Apache Spark, Flink, Storm, and Kafka (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787289864 , 1787289869
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Storm (Electronic resource) ; Big data ; Data mining ; Electronic data processing ; Distributed processing ; Management ; Electronic books ; Electronic books ; local
    Abstract: A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to dep...
    Note: Description based on online resource; title from title page (Safari, viewed October 31, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787281202/?ar
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  • 43
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    Practical data science cookbook : practical recipes on data pre-processing, analysis and visualization using R and Python (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787129627
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Object-oriented programming (Computer science) ; Data mining ; Mathematical statistics ; Data processing ; Python (Computer program language) ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis-R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization Downloa...
    Note: Previous edition published: 2014. - Description based on online resource; title from title page (Safari, viewed July 26, 2017)
    URL: lizenzpflichtig
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  • 44
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    Learning Kibana 5.0 : exploit the visualization capabilities of Kibana and build powerful interactive dashboards (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786465795 , 1786465795
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Information visualization ; Dashboards (Management information systems) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Exploit the visualization capabilities of Kibana and build powerful interactive dashboards About This Book Introduction to data-driven architecture and the Elastic stack Build effective dashboards for data visualization and explore datasets with Elastic Graph A comprehensive guide to learning scalable data visualization techniques in Kibana Who This Book Is For If you are a developer, data visualization engineer, or data scientist who wants to get the best of data visualization at scale then this book is perfect for you. A basic understanding of Elasticsearch and Logstash is required to make the best use of this book. What You Will Learn How to create visualizations in Kibana Ingest log data, structure an Elasticsearch cluster, and create visualization assets in Kibana Embed Kibana visualization on web pages Scaffold, develop, and deploy new Kibana & Timelion customizations Build a metrics dashboard in Timelion based on time series data Use the Graph plugin visualization feature and leverage a graph query Create, implement, package, and deploy a new custom plugin Use Prelert to solve anomaly detection challenges In Detail Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time. In this book, you'll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it's logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin. By the end of this book you will have all the information needed to take your Elastic stack sk...
    Note: Description based on online resource; title from cover (Safari, viewed March 9, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781786463005/?ar
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    Learning Apache Apex : Real-time streaming applications with Apex (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788294119 , 1788294114
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Apache Apex ; Data mining ; Big data
    Note: Description based on online resource; title from title page (viewed January 9, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788296403/?ar
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  • 46
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    Splunk : enterprise operational intelligence delivered : demystify big data and discover how to bring operational intelligence to your data to revolutionize your work (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787288256 , 1787288250
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Enterprise resource planning ; Management information systems ; Electronic data processing ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Demystify Big Data and discover how to bring operational intelligence to your data to revolutionize your work About This Book Get maximum use out of your data with Splunk's exceptional analysis and visualization capabilities Analyze and understand your operational data skillfully using this end-to-end course Full coverage of high-level Splunk techniques such as advanced searches, manipulations, and visualization Who This Book Is For This course is for software developers who wish to use Splunk for operational intelligence to make sense of their machine data. The content in this course will appeal to individuals from all facets of business, IT, security, product, marketing, and many more What You Will Learn Install and configure the latest version of Splunk. Use Splunk to gather, analyze, and report data Create Dashboards and Visualizations that make data meaningful Model and accelerate data and perform pivot-based reporting Integrate advanced JavaScript charts and leverage Splunk's APIs Develop and Manage apps in Splunk Integrate Splunk with R and Tableau using SDKs In Detail Splunk is an extremely powerful tool for searching, exploring, and visualizing data of all types. Splunk is becoming increasingly popular, as more and more businesses, both large and small, discover its ease and usefulness. Analysts, managers, students, and others can quickly learn how to use the data from their systems, networks, web traffic, and social media to make attractive and informative reports. This course will teach everything right from installing and configuring Splunk. The first module is for anyone who wants to manage data with Splunk. You'll start with very basics of Splunk - installing Splunk - before then moving on to searching machine data with Splunk. You will gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields. With more than 70 recipes on hand in the second module that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. Dive deep into Splunk to find the most efficient solution to your data problems in the third module. Create the robust Splunk solutions you need to make informed decisions in big data machine analytics. From visualizations to enterprise integration,...
    Note: "Learning path.". - Includes bibliographical references and index. - Description based on online resource; title from cover (viewed April 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787288256/?ar
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    Practical data wrangling : expert techniques for transforming your raw data into a valuable source for analytics (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787283671 , 1787283674
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Get simple examples and real-life data wrangling solutions for data pre-processing Who This Book Is For If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. As this book covers both R and Python, some understanding of them will be beneficial. What You Will Learn Read a csv file into python and R, and print out some statistics on the data Gain knowledge of the data formats and programming structures involved in retrieving API data Make effective use of regular expressions in the data wrangling process Explore the tools and packages available to prepare numerical data for analysis Find out how to have better control over manipulating the structure of the data Create a dexterity to programmatically read, audit, correct, and shape data Write and complete programs to take in, format, and output data sets In Detail Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You'll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You'll work with different data structures and acquire and parse data from various locations. You'll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the en...
    Note: Description based on online resource; title from title page (Safari, viewed December 19, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787286139/?ar
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    Mastering Apache Spark 2.x : scalable analytics faster than ever (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785285226 , 178528522X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Information retrieval ; Electronic books ; Electronic books ; local
    Abstract: Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs ...
    Note: Previous edition published: 2015. - Description based on online resource; title from title page (Safari, viewed August 14, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781786462749/?ar
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    Implementing Qlik Sense : design, develop, and validate BI solutions for consultants (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786466600 , 1786466600
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Management information systems ; Business intelligence ; Computer programs ; Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Become a full-fledged Qlik Sense Consultant with the help of this unique guide About This Book Become a successful Qlik consultant with the help of this insightful guide Build what is in line as well as exceeding your customer's expectations from your Qlik Sense solutions using this highly practical guide Build result-driven optimized BI solutions using Qlik with the help of industry examples Who This Book Is For If you have basic familiarity with Qlik Sense and want to upgrade your skills to become a full-fledged Qlik Consultant, this book is for you. With this book, you will be able to create efficient business intelligence solutions that would fetch client satisfaction, and in turn, more projects. What You Will Learn Understand the importance and expectations of a consultant's role Engage with the customer to understand the ir goals and future objectives Design the optimum architecture, using the best practices for the development and implementation of your projects Ensure successful adoption using real-life examples to make your learning complete Learn about the important stages of a Qlik project's life cycle In Detail Qlik Sense is a leading platform for business intelligence (BI) solutions. Qlik Sense helps organizations in making informed decisions based on the data they have. This book will teach you how to effectively use Qlik for optimum customer satisfaction. You will undergo a metamorphosis from a developer to a consultant who is capable of building the most suitable BI solutions for your clients. The book will take you through several business cases ? this will give you enough insight to understand the needs of the client clearly and build a BI solution that meets or exceeds their expectations. Starting from the pre-project activities, you will go to the actual execution of the project, the implementation, and even maintenance. This book will give you all the information you need - from the strategy to requirement gathering to implementing BI solutions using Qlik Sense. The book will empower you to take the right decisions in tricky and diffi cult situations while developing analytics and dashboards. Style and approach This book will be a hands-on guide that will teach you all the what-to-do's, when-to-do's, and how-to-do's for becoming a successful Qlik Sense Consultant. With the help of various business scenarios, the book will cover real-world problems that you can relate to. Various solutions in the book will be backed up by t...
    Note: Description based on online resource; title from title page (Safari, viewed November 10, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781786460448/?ar
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  • 50
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    Learning Spark SQL : architect streaming analytics and machine learning solutions (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785887352 , 1785887351
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Big data ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API About This Book Learn about the design and implementation of streaming applications, machine learning pipelines, deep learning, and large-scale graph processing applications using Spark SQL APIs and Scala. Learn data exploration, data munging, and how to process structured and semi-structured data using real-world datasets and gain hands-on exposure to the issues and challenges of working with noisy and "dirty" real-world data. Understand design considerations for scalability and performance in web-scale Spark application architectures. Who This Book Is For If you are a developer, engineer, or an architect and want to learn how to use Apache Spark in a web-scale project, then this is the book for you. It is assumed that you have prior knowledge of SQL querying. A basic programming knowledge with Scala, Java, R, or Python is all you need to get started with this book. What You Will Learn Familiarize yourself with Spark SQL programming, including working with DataFrame/Dataset API and SQL Perform a series of hands-on exercises with different types of data sources, including CSV, JSON, Avro, MySQL, and MongoDB Perform data quality checks, data visualization, and basic statistical analysis tasks Perform data munging tasks on publically available datasets Learn how to use Spark SQL and Apache Kafka to build streaming applications Learn key performance-tuning tips and tricks in Spark SQL applications Learn key architectural components and patterns in large-scale Spark SQL applications In Detail In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help yo...
    Note: Description based on online resource; title from title page (viewed October 3, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785888359/?ar
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  • 51
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    Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784393342 , 1784393347
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Management ; Data processing ; Machine learning ; Information visualization ; Data mining ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed exp...
    Note: Description based on online resource; title from title page (Safari, viewed November 14, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781784393878/?ar
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    R data mining : implement data mining techniques through practical use cases and real-world datasets (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787129238 , 1787129233
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 530
    Keywords: Data mining ; R (Computer program language) ; R ; Data Mining ; R ; Data Mining
    Note: Description based on online resource; title from title page (Safari, viewed January 9, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781787124462/?ar
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    Qlik sense : advanced data visualization for your organization : learning path : create smart data visualizations and predictive analytics solutions (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788998727 , 1788998723
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Management information systems ; Business intelligence ; Computer programs ; Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Perform Interactive Data Analysis with Smarter Visualizations and Support your Enterprise-wide Analytical Needs About This Book Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Who This Book Is For This course is for anyone who wishes to understand and utilize the various new approaches to business intelligence actively in their business practice. Knowing the basics of business intelligence concepts would be helpful when picking up this course, but is not mandatory. What You Will Learn Build simple visualization models with Rattle and Qlik Sense Desktop Get to grips with the life cycle and new visualization functions of a Qlik Sense application Discover simple ways to examine data and get it ready for analysis Visualize your data with Qlik Sense's engaging and informative graphs Build efficient and responsive Associative Models Optimize Qlik Sense for sales, human resources, and demographic data discovery Explore various tips and tricks of navigation for the Qlik Sense® front end Develop creative extensions for your Qlik Sense® dashboard In Detail Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business. This course begins by introducing you to the features and functions of the most modern edition of Qlik Sense so you get to grips with the application. The course will teach you how to administer the data architecture in Qlik Sense, enabling you to customize your own Qlik Sense application for your business intelligence needs. It also contains numerous recipes to help you overcome challenging situations while creating fully featured desktop applications in Qlik Sense. It explains how to combine Rattle and Qlik Sense Desktop to apply predictive analytics to your data to develop real-world interactive data applications. The course includes premium content from three of our most popular books: Learning Qlik Sense: The Official Guide Second Edition Qlik Sense Cookbook Predictive Analytics using Rattle and Qlik Sense On completion of this course, you will be self-sufficient in improving your data analysis and will know how to apply predictive analytics to your datasets. Through this course,...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed January 26, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788994927/?ar
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    Learning Pentaho data integration 8 CE : an end-to-end guide to exploring, transforming, and integrating your data across multiple sources (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788290074 , 1788290070
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Data integration (Computer science) ; Data mining ; Decision support systems ; Database management ; Electronic books ; Electronic books ; local
    Abstract: Get up and running with the Pentaho Data Integration tool using this hands-on, easy-to-read guide About This Book Manipulate your data by exploring, transforming, validating, and integrating it using Pentaho Data Integration 8 CE A comprehensive guide exploring the features of Pentaho Data Integration 8 CE Connect to any database engine, explore the databases, and perform all kind of operations on relational databases Who This Book Is For This book is a must-have for software developers, business intelligence analysts, IT students, or anyone involved or interested in developing ETL solutions. If you plan on using Pentaho Data Integration for doing any data manipulation task, this book will help you as well. This book is also a good starting point for data warehouse designers, architects, or anyone who is responsible for data warehouse projects and needs to load data into them. What You Will Learn Explore the features and capabilities of Pentaho Data Integration 8 Community Edition Install and get started with PDI Learn the ins and outs of Spoon, the graphical designer tool Learn to get data from all kind of data sources, such as plain files, Excel spreadsheets, databases, and XML files Use Pentaho Data Integration to perform CRUD (create, read, update, and delete) operations on relationaldatabases Populate a data mart with Pentaho Data Integration Use Pentaho Data Integration to organize files and folders, run daily processes, deal with errors, and more In Detail Pentaho Data Integration(PDI) is an intuitive and graphical environment packed with drag-and-drop design and powerful Extract-Tranform-Load (ETL) capabilities. This book shows and explains the new interactive features of Spoon, the revamped look and feel, and the newest features of the tool including transformations and jobs Executors and the invaluable Metadata Injection capability. We begin with the installation of PDI software and then move on to cover all the key PDI concepts. Each of the chapter introduces new features, enabling you to gradually get practicing with the tool. First, you will learn to do all kind of data manipulation and work with simple plain files. Then, the book teaches you how you can work with relational databases inside PDI. Moreover, you will be given a primer on data warehouse concepts and you will learn how to load data in a data warehouse. During the course of this book, you will be familiarized with its intuitive, graphical and drag-and-drop design envir...
    Note: Description based on online resource; title from title page (Safari, viewed January 12, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788292436/?ar
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    Stream Analytics with Microsoft Azure : real-time data processing for quick insights using Azure Stream Analytics (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788390620 , 1788390628
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Microsoft Azure (Computing platform) ; Cloud computing ; Data mining
    Note: Description based on online resource; title from title page (viewed January 10, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788395908/?ar
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    Learning Google BigQuery : a beginner's guide to mining massive datasets through interactive analysis (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787286290 , 1787286290
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Google BigQuery ; Google Analytics ; Data mining
    Note: Description based on online resource; title from title page (viewed January 31, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781787288591/?ar
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  • 57
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    Learning Elasticsearch : distributed real-time search and analytics with Elasticsearch 5.x (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787129917 , 1787129918
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Lucene (Electronic resource) ; Web search engines ; Data mining ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Store, search, and analyze your data with ease using Elasticsearch 5.x About This Book Get to grips with the basics of Elasticsearch concepts and its APIs, and use them to create efficient applications Create large-scale Elasticsearch clusters and perform analytics using aggregation This comprehensive guide will get you up and running with Elasticsearch 5.x in no time Who This Book Is For If you want to build efficient search and analytics applications using Elasticsearch, this book is for you. It will also benefit developers who have worked with Lucene or Solr before and now want to work with Elasticsearch. No previous knowledge of Elasticsearch is expected. What You Will Learn See how to set up and configure Elasticsearch and Kibana Know how to ingest structured and unstructured data using Elasticsearch Understand how a search engine works and the concepts of relevance and scoring Find out how to query Elasticsearch with a high degree of performance and scalability Improve the user experience by using autocomplete, geolocation queries, and much more See how to slice and dice your data using Elasticsearch aggregations. Grasp how to use Kibana to explore and visualize your data Know how to host on Elastic Cloud and how to use the latest X-Pack features such as Graph and Alerting In Detail Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments. Style and approach This comprehensive guide will get you started with Elasticsearch 5.x, so you build a solid understanding of the basics. Every topic is explained in depth and ...
    Note: Description based on online resource; title from title page (Safari, viewed August 7, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787128453/?ar
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    Learning Jupyter : learn how to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using Project Jupyter (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785889455 , 1785889451
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Science ; Data processing ; Data mining ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Learn how to write code, mathematics, graphics, and output, all in a single document, as well as in a web browser using Project Jupyter About This Book Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc Who This Book Is For This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser. Data science professionals will also find this book very useful to perform technical and scientific computing in a graphical, agile manner. What You Will Learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you in the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter In Detail Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we'll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book. Style and approach This comprehensive practical guide will teach you how to work with the Jupyter Notebook system. It demonstrates the integration of various programming languages with Jupyter Noteb...
    Note: Description based on online resource; title from cover (Safari, viewed January 5, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785884870/?ar
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    Splunk developer's guide : learn the a to z of building excellent Splunk applications with the latest techniques using this comprehensive guide (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785883552 , 1785883550
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Professional expertise distilled
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Electronic books ; Electronic books ; local
    Abstract: Learn the A to Z of building excellent Splunk applications with the latest techniques using this comprehensive guide About This Book This is the most up-to-date book on Splunk 6.3 for developers Get ahead of being just a Splunk user and start creating custom Splunk applications as per your needs Your one-stop-solution to Splunk application development Who This Book Is For This book is for those who have some familiarity with Splunk and now want to learn how to develop an efficient Splunk application. Previous experience with Splunk, writing searches, and designing basic dashboards is expected. What You Will Learn Implement a Modular Input and a custom D3 data visualization Create a directory structure and set view permissions Create a search view and a dashboard view using advanced XML modules Enhance your application using eventtypes, tags, and macros Package a Splunk application using best practices Publish a Splunk application to the Splunk community In Detail Splunk provides a platform that allows you to search data stored on a machine, analyze it, and visualize the analyzed data to make informed decisions. The adoption of Splunk in enterprises is huge, and it has a wide range of customers right from Adobe to Dominos. Using the Splunk platform as a user is one thing, but customizing this platform and creating applications specific to your needs takes more than basic knowledge of the platform. This book will dive into developing Splunk applications that cater to your needs of making sense of data and will let you visualize this data with the help of stunning dashboards. This book includes everything on developing a full-fledged Splunk application right from designing to implementing to publishing. We will design the fundamentals to build a Splunk application and then move on to creating one. During the course of the book, we will cover application data, objects, permissions, and more. After this, we will show you how to enhance the application, including branding, workflows, and enriched data. Views, dashboards, and web frameworks are also covered. This book will showcase everything new in the latest version of Splunk including the latest data models, alert actions, XML forms, various dashboard enhancements, and visualization options (with D3). Finally, we take a look at the latest Splunk cloud applications, advanced integrations, and development as per the latest release. Style and approach This book is an easy-to-follow guide with lots of...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed February 5, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785882371/?ar
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    Principles of data science : learn the techniques and math you need to start making sense of your data (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785888922 , 1785888927
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; Quantitative research ; Electronic books ; Electronic books ; local
    Abstract: Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking-and answering-complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create pow...
    Note: Includes index. - Description based on online resource; title from cover (viewed January 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785887918/?ar
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    Apache Spark for data science cookbook : overinsightful 90 recipes to get lightning-fast analytics with Apache Spark (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785288807 , 1785288806
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Information retrieval ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficul...
    Note: Description based on online resource; title from cover (Safari, viewed January 17, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785880100/?ar
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    Splunk operational intelligence cookbook : over 70 practical recipes to gain operational data intelligence with Splunk Enterprise (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785287497 , 1785287494
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Quick answers to common problems
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Electronic books ; Electronic books ; local
    Abstract: Over 70 practical recipes to gain operational data intelligence with Splunk Enterprise About This Book This is the most up-to-date book on Splunk 6.3 and teaches you how to tackle real-world operational intelligence scenarios efficiently Get business insights using machine data using this easy-to-follow guide Search, monitor, and analyze your operational data skillfully using this recipe-based, practical guide Who This Book Is For This book is intended for users of all levels who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of business, IT, security, product, marketing, and many more! Also, existing users of Splunk who want to upgrade and get up and running with Splunk 6.3 will find this book invaluable. What You Will Learn Use Splunk to gather, analyze, and report on data Create dashboards and visualizations that make data meaningful Build an operational intelligence application with extensive features and functionality Enrich operational data with lookups and workflows Model and accelerate data and perform pivot-based reporting Build real-time, scripted, and other intelligence-driven alerts Summarize data for longer term trending, reporting, and analysis Integrate advanced JavaScript charts and leverage Splunk's API In Detail Splunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 70 recipes that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You'll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You'll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organization. Throughout the book, you'll dive deeper into Splunk, explore data models and pivots to extend your intel...
    Note: Previous edition: 2014. - Includes index. - Description based on online resource; title from cover (Safari, viewed June 24, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785284991/?ar
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    Real-time big data analytics : design, process, and analyze large sets of complex data in real time (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784397401 , 1784397407
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Big data ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Design, process, and analyze large sets of complex data in real time About This Book Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm Implement strategies to solve the challenges of real-time data processing Load datasets, build queries, and make recommendations using Spark SQL Who This Book Is For If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you. What You Will Learn Explore big data technologies and frameworks Work through practical challenges and use cases of real-time analytics versus batch analytics Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm Optimize and tune Apache Storm for varied workloads and production deployments Process and stream data with Amazon Kinesis and Elastic MapReduce Perform interactive and exploratory data analytics using Spark SQL Develop common enterprise architectures/applications for real-time and batch analytics In Detail Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. Moving on, we'll familiarize you with ?Amazon Kinesis? for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed March 23, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781784391409/?ar
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    Splunk best practices : design, implement, and publish custom Splunk applications by following best practices (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785289415 , 1785289411
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Professional expertise distilled
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Design, implement, and publish custom Splunk applications by following best practices About This Book This is the most up-to-date guide on the market and will help you finish your tasks faster, easier, and more efficiently. Highly practical guide that addresses common and not-so-common pain points in Splunk. Want to explore shortcuts to perform tasks more efficiently with Splunk? This is the book for you! Who This Book Is For This book is for administrators, developers, and search ninjas who have been using Splunk for some time. A comprehensive coverage makes this book great for Splunk veterans and newbies alike. What You Will Learn Use Splunk effectively to gather, analyze, and report on operational data throughout your environment Expedite your reporting, and be empowered to present data in a meaningful way Create robust searches, reports, and charts using Splunk Modularize your programs for better reusability. Build your own Splunk apps and learn why they are important Learn how to integrate with enterprise systems Summarize data for longer term trending, reporting, and analysis In Detail This book will give you an edge over others through insights that will help you in day-to-day instances. When you're working with data from various sources in Splunk and performing analysis on this data, it can be a bit tricky. With this book, you will learn the best practices of working with Splunk. You'll learn about tools and techniques that will ease your life with Splunk, and will ultimately save you time. In some cases, it will adjust your thinking of what Splunk is, and what it can and cannot do. To start with, you'll get to know the best practices to get data into Splunk, analyze data, and package apps for distribution. Next, you'll discover the best practices in logging, operations, knowledge management, searching, and reporting. To finish off, we will teach you how to troubleshoot Splunk searches, as well as deployment, testing, and development with Splunk. Style and approach If you're stuck or want to find a better way to work with Splunk environment, this book will come handy. This easy-to-follow, insightful book contains step-by-step instructions and examples and scenarios that you will connect to. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com . If you purchased this book elsewhere, you can visit http://www.PacktPub.com/supp...
    Note: Description based on online resource; title from cover (Safari, viewed October 6, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785281396/?ar
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    Splunk essentials : a fast-paced and practical guide to demystifying big data and transforming it into operational intelligence (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785882135 , 1785882139
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Professional expertise distilled
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Electronic books ; Electronic books ; local
    Abstract: A fast-paced and practical guide to demystifying big data and transforming it into operational intelligence About This Book Want to get started with Splunk to analyze and visualize machine data? Open this book and step into the world of Splunk. Leverage the exceptional analysis and visualization capabilities to make informed decisions for your business This easy-to-follow, practical book can be used by anyone, even if you have never managed any data before Who This Book Is For This book will be perfect for you if you are a Software engineer or developer or System administrators or Business analyst who seek to correlate machine data with business metrics and provide intuitive real-time and statistical visualizations. Some knowledge or experience of previous versions of Splunk will be helpful but not essential. What You Will Learn Install and configure Splunk Gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields Be comfortable with the Search Processing Language and get to know the best practices in writing search queries Create stunning and powerful dashboards Be proactive by implementing alerts and scheduled reports Use the Splunk SDK and integrate Splunk data into other applications Implement the best practices in using Splunk. In Detail Splunk is a search, analysis, and reporting platform for machine data, which has a high adoption on the market. More and more organizations want to adopt Splunk to use their data to make informed decisions. This book is for anyone who wants to manage data with Splunk. You'll start with very basics of Splunk- installing Splunk-and then move on to searching machine data with Splunk. You will gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields. After this, you will learn to create various reports, XML forms, and alerts. You will then continue using the Pivot Model to transform the data models into visualization. You will also explore visualization with D3 in Splunk. Finally you'll be provided with some real-world best practices in using Splunk. Style and approach This fast-paced, example-rich guide will help you analyze and visualize machine data with Splunk through simple, practical instructions. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.Packt...
    Note: Previous edition: 2015. - Description based on online resource; title from cover (Safari, viewed October 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785889462/?ar
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    Learning Splunk Web Framework : take your analytics online with the ease and power of the Splunk Web Framework (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786461452 , 1786461455
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Professional expertise distilled
    Keywords: Big data ; Computer programs ; Data mining ; Automatic data collection systems ; Electronic books ; Electronic books ; local
    Abstract: Take your analytics online with the ease and power of the Splunk Web Framework About This Book Want to build rich applications on the Web using Splunk? This book will be your ultimate guide! Learn to use web framework components with the help of this highly practical, example-rich guide Perform excellent Splunk analytics on the Web and bring that knowledge to your own projects Who This Book Is For This book will cater to Splunk developers and administrators who now wish to further their knowledge with Splunk Web Framework and learn to improve the way they present and visualize data in Splunk. A basic knowledge of JavaScript will be beneficial but is not a prerequisite. What You Will Learn Master the fundamentals of Splunk Web Framework Start thinking of Splunk as a complete development platform to build user-friendly apps Extend the functionality of your apps using SimpleXML techniques Set up dashboard layouts, navigation, and menus in your apps Create simple dashboard elements including charts and tables Master the art of interacting with searches and dashboards Integrate SplunkJS to add visual appeal to your website In Detail Building rich applications on the Web using Splunk is now simpler than ever before with the Splunk Web Framework. It empowers developers to build their own web applications with custom dashboards, tables, charts, form searches, and other functionalities in the datasets at their disposal. The book will start with the fundamentals of the Splunk Web Framework, teaching you the secrets of building interesting and user-friendly applications. In the first application, you will learn to analyze and monitor traffic hitting the NASA website and learn to create dashboards for it. You will then learn additional, and more detailed, techniques to enhance the functionalities of the app such as dashboards and forms, editing simple XML, using simple XML extensions, tokens, post-process searches, dynamic drill-downs, the Splunk Web Framework and REST API, and much more. The second app will use historical stock market data and will create custom dashboards using Splunk Web Framework; the book will now cover important topics such as creating HTML dashboards, enhancing the visual appeal of the app using CSS, and moving your app with SplunkJS. The book will provide different and interesting examples instead of the usual ?Log, Index, Search, and Graph? so that Splunk will be the first tool readers think of to resolve a problem. Style and app...
    Note: Description based on online resource; title from cover (viewed September 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781786462947/?ar
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    Mastering data mining with Python : find patterns hidden in your data : learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785885914 , 178588591X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Community experience distilled
    Keywords: Data mining ; Python (Computer program language) ; Business planning ; Data processing ; Electronic books ; local
    Abstract: Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques About This Book Dive deeper into data mining with Python ? don't be complacent, sharpen your skills! From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries Who This Book Is For This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you! What You Will Learn Explore techniques for finding frequent itemsets and association rules in large data sets Learn identification methods for entity matches across many different types of data Identify the basics of network mining and how to apply it to real-world data sets Discover methods for detecting the sentiment of text and for locating named entities in text Observe multiple techniques for automatically extracting summaries and generating topic models for text See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set In Detail Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy ? without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software e...
    Note: Includes index. - Description based on online resource; title from cover (viewed September 13, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785889950/?ar
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    Spark for data science : analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785884771 , 1785884778
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ?Big Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.
    Note: Description based on online resource; title from cover (Safari, viewed October 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785885655/?ar
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    Big data analytics : a handy reference guide for data analysts and data scientists to help obtain value from big data analytics using Spark on Hadoop clusters (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785889707 , 1785889702
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Big data ; Data mining ; Relational databases ; Electronic books ; Electronic books ; local
    Abstract: A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components ? Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components ? HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learni...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed October 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785884696/?ar
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    Simulation for data science with R : harness actionable insights from your data with computational statistics and simulations using R (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785885877 , 1785885871
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: R (Computer program language) ; Data mining ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Harness actionable insights from your data with computational statistics and simulations using R About This Book Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulation Who This Book Is For This book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required. What You Will Learn The book aims to explore advanced R features to simulate data to extract insights from your data. Get to know the advanced features of R including high-performance computing and advanced data manipulation See random number simulation used to simulate distributions, data sets, and populations Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations Applications to design statistical solutions with R for solving scientific and real world problems Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more. In Detail Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to ai...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed July 20, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785881169/?ar
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    Mastering text mining with R : master text-taming techniques and build effective text-processing applications with R (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781782174707 , 1782174702
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 250
    RVK:
    ST 530
    Keywords: Text processing (Computer science) ; R (Computer program language) ; Data mining ; Application software Development
    Note: Description based on online resource; title from cover (Safari, viewed January 25, 2017). - Includes index
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781783551811/?ar
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    Scala for data science : leverage the power of Scala to build scalable, robust data science applications (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785289385 , 1785289381
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    DDC: 005.133
    Keywords: Scala (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided Who This Book Is For If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions. What You Will Learn Transform and filter tabular data to extract features for machine learning Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines Read, transform, and write data to both SQL and NoSQL databases in a functional manner Write robust routines to query web APIs Read data from web APIs such as the GitHub or Twitter API Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive In Detail Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectu...
    Note: Description based on online resource; title from cover page (Safari, viewed February 12, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785281372/?ar
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    Learning Scrapy : learn the art of efficient web scraping and crawling with Python (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784390914 , 1784390917
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Learn the art of efficient web scraping and crawling with Python About This Book Extract data from any source to perform real time analytics. Full of techniques and examples to help you crawl websites and extract data within hours. A hands-on guide to web scraping and crawling with real-life problems and solutions Who This Book Is For If you are a software developer, data scientist, NLP or machine-learning enthusiast or just need to migrate your company's wiki from a legacy platform, then this book is for you. It is perfect for someone , who needs instant access to large amounts of semi-structured data effortlessly. What You Will Learn Understand HTML pages and write XPath to extract the data you need Write Scrapy spiders with simple Python and do web crawls Push your data into any database, search engine or analytics system Configure your spider to download files, images and use proxies Create efficient pipelines that shape data in precisely the form you want Use Twisted Asynchronous API to process hundreds of items concurrently Make your crawler super-fast by learning how to tune Scrapy's performance Perform large scale distributed crawls with scrapyd and scrapinghub In Detail This book covers the long awaited Scrapy v 1.0 that empowers you to extract useful data from virtually any source with very little effort. It starts off by explaining the fundamentals of Scrapy framework, followed by a thorough description of how to extract data from any source, clean it up, shape it as per your requirement using Python and 3rd party APIs. Next you will be familiarised with the process of storing the scrapped data in databases as well as search engines and performing real time analytics on them with Spark Streaming. By the end of this book, you will perfect the art of scarping data for your applications with ease Style and approach It is a hands on guide, with first few chapters written as a tutorial, aiming to motivate you and get you started quickly. As the book progresses, more advanced features are explained with real world examples that can be reffered while developing your own web applications.
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed February 12, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781784399788/?ar
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    Learning Qlik Sense : the official guide : get the most out of your Qlik Sense investment with the latest insight and guidance direct from the Qlik Sense team (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785884054 , 1785884050
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Professional expertise distilled
    Keywords: Management information systems ; Business intelligence ; Computer programs ; Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Get the most out of your Qlik Sense investment with the latest insight and guidance direct from the Qlik Sense team About This Book Updated with new coverage on Qlik Cloud, Qlik Sense Modeling, and Extending the Qlik Analytic Platform. Get insider insight on Qlik Sense and its new approach to business intelligence Explore practical demonstrations for utilizing Qlik Sense to discover data for sales, human resources, and more Who This Book Is For Learning Qlik Sense®: The Official Guide Second Edition is for anyone seeking to understand and utilize the revolutionary new approach to business intelligence offered by Qlik Sense. Familiarity with the basics of business intelligence will be helpful when picking up this book, but not essential. What You Will Learn Understand the vision behind the creation of Qlik Sense, and the promise that data discovery offers to you and your organization Get to grips with the life cycle of a Qlik Sense application Load and manage your data for app creation Visualize your data with Qlik Sense's engaging and informative graphing Administer your Qlik Sense system and monitor its security Build efficient and responsive Associative Models Extend the Qlik Analytic Platform with the Dev Hub Optimize Qlik Sense for sales, human resources, and demographic data discovery In Detail The intuitive and powerful Qlik Sense visual analytics software allows anyone to engage in data discovery, to explore your data, and find meaningful insights to empower your business. Qlik Sense lets you easily create personalized reports and visualizations and reveal essential connections to show new opportunities from every angle. Written by members of the Qlik Sense team, this book is the official guide from Qlik to understanding and using their powerful new product with fully updated coverage to the latest features of the most modern edition of Qlik Sense. Benefit from the vision behind the development of Qlik Sense and get to grips with how Qlik Sense can empower you as a data discovery consumer. Learn how to create your own applications for Qlik Sense to customize it to meet your personal needs for business intelligence, and how to oversee and administer the Qlik Sense data architecture. Finally, explore utilizing Qlik Sense to uncover essential data, with practical examples on finding and visualizing intelligence for sales figures, human resources information, travel expense tracking, and demographic data discovery. Style and approach This b...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed January 21, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781785887161/?ar
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    Mastering data analysis with R : gain clear insights into your data and solve real-world data science problems with R--from data munging to modeling and visualization (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781783982035 , 1783982039
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Data mining ; R (Computer program language) ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Gain sharp insights into your data and solve real-world data science problems with R-from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed October 21, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781783982028/?ar
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    Thinking ahead-- essays on big data, digital revolution, and participatory market society (2015)
    Cham : Springer
    Details
    ISBN: 9783319150789
    Language: English
    Pages: Online-Ressource (xiii, 194 pages) , color illustrations
    Edition: Online-Ausg.
    Parallel Title: Print version Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society
    DDC: 005.7
    Keywords: Internet Political aspects ; Data mining ; Big data ; Internet Social aspects ; Big data.. ; Data mining.. ; Internet ; Social aspects.. ; Internet ; Political aspects ; Electronic books
    Abstract: The rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories - in particular sociological and economic ones - are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history - can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and
    Description / Table of Contents: About the Author; Preface; Contents; Chapter 1 Introduction--Have We Opened Pandora's Box?; 1.1 Global Financial, Economic and Public Spending Crisis; 1.2 Need of a ``Knowledge Accelerator''; 1.3 We are Experiencing a Digital Revolution; 1.4 Threats to the Average Citizen; 1.5 Threats so Big that One Cannot Even Talk About Them; 1.6 Are we Entering an Age of Discrimination?; 1.7 Threats to Companies; 1.8 Political and Societal Risks; 1.9 Are the Secret Services Democratically well Controlled?; 1.10 What Kind of Society are we Heading to?; 1.11 ``Big Governments'' Fueled by ``Big Data''
    Description / Table of Contents: 1.12 We Must Move Beyond September 111.13 What Needs to be Done; 1.14 A Better Future, Based on Self-Regulation; References; Chapter 2 Lost Robustness; 2.1 Understanding Complex Systems; 2.2 Criticality and Lack of Transparency; 2.3 Acceleration and De-Compartmentalization; 2.4 Systemic Stability and Trust; 2.5 Utilizing Control Features of Complex Systems; 2.6 Author Information; Chapter 3 How and Why Our Conventional Economic Thinking Causes Global Crises; 3.1 ``More Networking Is Good and Reduces Risks''; 3.2 ``The Economy Tends Towards an Equilibrium State''
    Description / Table of Contents: 3.3 ``Individuals and Companies Decide Rationally''3.4 ``Selfish Behavior Optimizes the Systemic Performance and Benefits Everyone''; 3.5 ``Financial Markets Are Efficient''; 3.6 ``More Information and Financial Innovations Are Good''; 3.7 ``More Liquidity Is Better''; 3.8 ``All Agents can Be Treated as if Acting the Same Way''; 3.9 ``Regulation can Fix the Imperfections of Economic Systems''; 3.10 ``Moral Behavior Is Good for Others, but Bad for Oneself''; 3.11 Summary; Further Reading; Chapter 4 ``Networked Minds'' Require a Fundamentally New Kind of Economics
    Description / Table of Contents: 4.1 Evolution of ``Friendliness''4.2 Networked Minds Create a Cooperative Human Species; 4.3 A Participatory Kind of Economy; Chapter 5 A New Kind of Economy is Born-Social Decision-Makers Beat the ``Homo Economicus'' ; 5.1 Outdated Theory, Outdated Institutions; 5.2 New Institutions for a Global Information Society; 5.3 Benefits of a Self-Regulating Economy; 5.4 Economics 2.0: Emergence of a Participatory Market Society; References; Further Reading; Chapter 6 Global Networks Must be Redesigned; 6.1 Living in a Hyperconnected World; 6.2 Our Intuition of Systemic Risks is Misleading
    Description / Table of Contents: 6.3 A Global Ticking Time Bomb?6.4 Global Networks Must be Redesigned; 6.5 Coming Era of Social Innovation; 6.6 Creating and Protecting Social Capital; Chapter 7 Big Data--A Powerful New Resource for the Twenty-first Century; 7.1 Data Sets Bigger than the Largest Library; 7.2 What Do Applications Look Like?; 7.3 The Potentials Are Great...; 7.4 ... but also the Implicit Risks; 7.5 The Digital Revolution Creates an Urgency to Act; 7.6 Europe can Become a Motor of Innovation for the Digital Era; References; Chapter 8 Google as God? Opportunities and Risks of the Information Age
    Description / Table of Contents: 8.1 Introduction
    Note: Includes bibliographical references
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    Book
    Thinking ahead : essays on big data, digital revolution, and participatory market society (2015)
    Cham : Springer
    Details
    ISBN: 3319150774 , 9783319150772
    Language: English
    Pages: XIII, 194 S. , Illustrationen
    Parallel Title: Online-Ausg. Helbing, Dirk, 1965 - Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society
    DDC: 004#DNB
    RVK:
    AP 15950
    RVK:
    MS 7965
    RVK:
    SR 850
    Keywords: Internet Political aspects ; Data mining ; Big data ; Internet Social aspects ; Big Data ; Informationsgesellschaft ; Informationswirtschaft ; Vernetzung ; Data Mining ; Social Media ; Sozialer Wandel
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    Kibana essentials : use the functionalities of Kibana to reveal insights from the data and build attractive visualizations and dashboards for real-world scenarios (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784392444 , 1784392448
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenarios About This Book Perform real-time data analytics and visualizations, on streaming data, using Kibana Build beautiful visualizations and dashboards with simplicity and ease without any type of coding involved Learn all the core concepts as well as detailed information about each component used in Kibana Who This Book Is For Whether you are new to the world of data analytics and data visualization or an expert, this book will provide you with the skills required to use Kibana with ease and simplicity for real-time data visualization of streaming data. This book is intended for those professionals who are interested in learning about Kibana,its installations, and how to use it . As Kibana provides a user-friendly web page, no prior experience is required. What You Will Learn Understand the basic concepts of elasticsearch used in Kibana along with step by step guide to install Kibana in Windows and Ubuntu Explore the functionality of all the components used in Kibana in detail, such as the Discover, Visualize, Dashboard,and Settings pages Analyze data using the powerful search capabilities of elasticsearch Understand the different types of aggregations used in Kibana for visualization Create and build different types of amazing visualizations and dashboards easily Create, save, share, embed, and customize the visualizations added to the dashboard Customize and tweak the advanced settings of Kibana to ensure ease of use In Detail With the increasing interest in data analytics and visualization of large data around the globe, Kibana offers the best features to analyze data and create attractive visualizations and dashboards through simple-to-use web pages. The variety of visualizations provided, combined with the powerful underlying elasticsearch capabilities will help professionals improve their skills with this technology. This book will help you quickly familiarize yourself to Kibana and will also help you to understand the core concepts of this technology to build visualizations easily. Starting with setting up of Kibana and elasticsearch in Windows and Ubuntu, you will then use the Discover page to analyse your data intelligently. Next, you will learn to use the Visualization page to create beautiful visualizations without the need for any coding. Then, you will learn how to use the Dashboard page to create a dashboard ...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed December 1, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781784394936/?ar
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  • 79
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    Learning ELK stack : build mesmerizing visualizations, and analytics from your logs and data using Elasticsearch, Logstash, and Kibana (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785886706 , 1785886703
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Information visualization ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Build mesmerizing visualizations, analytics, and logs from your data using Elasticsearch, Logstash, and Kibana About This Book Solve all your data analytics problems with the ELK stack Explore the power of Kibana4 search and visualizations built over Elasticsearch queries and learn about the features and plugins of Logstash Develop a complete data pipeline using the ELK stack Who This Book Is For If you are a developer or DevOps engineer interested in building a system that provides amazing insights and business metrics out of data sources, of various formats and types, using the open source technology stack that ELK provides, then this book is for you. Basic knowledge of Unix or any programming language will be helpful to make the most out of this book. What You Will Learn Install, configure, and run Elasticsearch, Logstash, and Kibana Understand the need for log analytics and the current challenges in log analysis Build your own data pipeline using the ELK stack Familiarize yourself with the key features of Logstash and the variety of input, filter, and output plugins it provides Build your own custom Logstash plugin Create actionable insights using charts, histograms, and quick search features in Kibana4 Understand the role of Elasticsearch in the ELK stack In Detail The ELK stack - Elasticsearch, Logstash, and Kibana, is a powerful combination of open source tools. Elasticsearch is for deep search and data analytics. Logstash is for centralized logging, log enrichment, and parsing. Kibana is for powerful and beautiful data visualizations. In short, the Elasticsearch ELK stack makes searching and analyzing data easier than ever before. This book will introduce you to the ELK (Elasticsearch, Logstash, and Kibana) stack, starting by showing you how to set up the stack by installing the tools, and basic configuration. You'll move on to building a basic data pipeline using the ELK stack. Next, you'll explore the key features of Logstash and its role in the ELK stack, including creating Logstash plugins, which will enable you to use your own customized plugins. The importance of Elasticsearch and Kibana in the ELK stack is also covered, along with various types of advanced data analysis, and a variety of charts, tables ,and maps. Finally, by the end of the book you will be able to develop full-fledged data pipeline using the ELK stack and have a solid understanding of the role of each of the components. Style and approach This book is a step-by-...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed December 14, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781785887154/?ar
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  • 80
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    Online Resource
    Python for secret agents : gather, analyze, and decode data to reveal hidden facts using Python, the perfect tool for all aspiring secret agents (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785285431 , 1785285432
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Community experience distilled
    Keywords: Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Gather, analyze, and decode data to reveal hidden facts using Python, the perfect tool for all aspiring secret agents About This Book Discover the essential features of Python programming: statements, variables, expressions, and many of the built-in data types Use Python's standard library to do more sophisticated data gathering and analysis Written by a Python programming expert, with over 35 years' experience as a consultant, teacher, author and software developer Who This Book Is For This book is for Secret Agents who have some exposure to Python. Our focus is on the Field Agents who are ready to do more sophisticated and complex programming in Python. We'll stick to simple statistics for the most part. A steady hand with a soldering iron is not required, but a skilled field agent should be able to assemble a working Arduino circuit to gather their own sensor data. What You Will Learn Upgrade Python to the latest version and discover its latest and greatest tools Use Python libraries to extract data from log files that are designed more for people to read than for automated analysis Summarize log files and extract meaningful information Gather data from social networking sites and leverage your experience of analyzing log files to summarize the data you find Extract text and images from social networking sites Parse the complex and confusing data structures in a PDF file to extract meaningful text that we can analyze Connect small, intelligent devices to our computer to use them as remote sensors Use Python to analyze measurements from sensors to calibrate them and use sensors efficiently In Detail Python is easy to learn and extensible programming language that allows any manner of secret agent to work with a variety of data. Agents from beginners to seasoned veterans will benefit from Python's simplicity and sophistication. The standard library provides numerous packages that move beyond simple beginner missions. The Python ecosystem of related packages and libraries supports deep information processing. This book will guide you through the process of upgrading your Python-based toolset for intelligence gathering, analysis, and communication. You'll explore the ways Python is used to analyze web logs to discover the trails of activities that can be found in web and database servers. We'll also look at how we can use Python to discover details of the social network by looking at the data available from social networking websites. Finally, ...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed December 22, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781785283406/?ar
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  • 81
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    Scala data analysis cookbook : navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784394998 , 1784394998
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Quick answers to common problems
    DDC: 005.133
    Keywords: Data mining ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin Scale up your data anlytics infrastructure with practical recipes for Scala machine learning Recipes for every stage of the data analysis process, from reading and collecting data to distributed analytics Who This Book Is For This book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis. What You Will Learn Familiarize and set up the Breeze and Spark libraries and use data structures Import data from a host of possible sources and create dataframes from CSV Clean, validate and transform data using Scala to pre-process numerical and string data Integrate quintessential machine learning algorithms using Scala stack Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysis In Detail This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX. Style and approach This book contains a rich set of recipes that covers the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala and Spark.
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed December 10, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781784396749/?ar
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  • 82
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    Online Resource
    Learning IPython for interactive computing and data visualization : get started with Python for data analysis and numerical computing in the Jupyter notebook (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781783986996 , 1783986999
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Community experience distilled
    DDC: 005.133
    Keywords: Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed November 11, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781783986989/?ar
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  • 83
    Online Resource
    Online Resource
    Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784398514 , 1784398519 , 9781784390792
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Professional expertise distilled
    DDC: 005.74
    Keywords: Windows Azure ; Machine learning ; Data mining
    Note: Description based on online resource; title from cover (Safari, viewed July 28, 2015). - Includes index
    URL: Volltext
    URL: https://learning.oreilly.com/library/view/-/9781784390792/?ar
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  • 84
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    Online Resource
    Implementing Splunk : a comprehensive guide to help you transform big data into valuable business insights with Splunk 6.2 (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781784399306 , 1784399302 , 9781784391607
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition
    Series Statement: Professional expertise distilled
    DDC: 005.7565
    Keywords: Big data ; Data mining ; Automatic data collection systems ; Data Mining ; Datenanalyse ; Big Data ; Big Data ; Datenanalyse ; Data Mining
    Note: Description based on online resource; title from cover (Safari, viewed August 13, 2015). - Includes index
    URL: Volltext
    URL: https://learning.oreilly.com/library/view/-/9781784391607/?ar
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