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  • 2015-2019  (8)
  • 1935-1939
  • 2019  (8)
  • Birmingham, UK : Packt Publishing  (8)
  • Information visualization  (8)
Datasource
Material
Language
Years
  • 2015-2019  (8)
  • 1935-1939
Year
Author, Corporation
Publisher
Keywords
  • 1
    Online Resource
    Online Resource
    Learning Tableau 2019 : tools for business intelligence, data prep, and visual analytics (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788838740 , 1788838742
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Tableau (Computer file) ; Information visualization ; Business intelligence ; Computer programs ; Electronic books ; Electronic books ; local
    Abstract: Create powerful data visualizations and unlock intelligent business insights with Tableau Key Features Explore all the latest Tableau 2019 features and redefine business analytics for your organization Create impressive data visualizations and interactive dashboards to get insights from your data Learn effective data storytelling to transform how your business leverages data and makes decisions Book Description Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics. This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis. By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making. What you will learn Develop stunning visualizations that explain complexity with clarity Explore the exciting new features of Tableau Desktop and Tableau Prep Connect to various data sources to bring all your data together Uncover techniques to prep and structure your data for easy analysis Create and use calculations to solve problems and enrich analytics Master advanced topics such as sets, LOD calcs, and much more Enable smart decisions with clustering, distribution, and forecasting Share your data stories to build a culture of trust and action Who this book is for This Tableau book is for anyone who wants to understand data. If you're new to Tableau, don't worry. This book builds on the foundations to help you understand how Tableau really works and then builds on that knowledge with practical examples before moving on to advanced techniques. Working experience...
    Note: Previous edition published: 2016. - Description based on online resource; title from title page (Safari, viewed May 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788839525/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Hands-on deep learning with Apache Spark : build and deploy distributed deep learning applications on Apache Spark (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788999700 , 1788999703
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Machine learning ; Electronic data processing ; Distributed processing ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications to intelligently handle large and complex datasets Book Description Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learn Understand the basics of deep learning Set up Apache Spark for deep learning Understand the principles of distribution modeling and different types of neural networks Obtain an understanding of deep learning algorithms Discover textual analysis and deep learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms Who this book is for If you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.
    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/-/9781788994613/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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  • 3
    Online Resource
    Online Resource
    Learn Chart.js : create interactive visualizations for the Web with Chart.js 2 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342154 , 1789342155
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Charts, diagrams, etc ; Computer programs ; JavaScript (Computer program language) ; Information visualization ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Design interactive graphics and visuals for your data-driven applications using the popular open-source Chart.js data visualization library. Key Features Harness the power of JavaScript, HTML, and CSS to create interactive visualizations Display quantitative information efficiently in the form of attractive charts by using Chart.js A practical guide for creating data-driven applications using open-source JavaScript library Book Description Chart.js is a free, open-source data visualization library, maintained by an active community of developers in GitHub, where it rates as the second most popular data visualization library. If you want to quickly create responsive Web-based data visualizations for the Web, Chart.js is a great choice. This book guides the reader through dozens of practical examples, complete with code you can run and modify as you wish. It is a practical hands-on introduction to Chart.js. If you have basic knowledge of HTML, CSS and JavaScript you can learn to create beautiful interactive Web Canvas-based visualizations for your data using Chart.js. This book will help you set up Chart.js in a Web page and show how to create each one of the eight Chart.js chart types. You will also learn how to configure most properties that override Chart's default styles and behaviors. Practical applications of Chart.js are exemplified using real data files obtained from public data portals. You will learn how to load, parse, filter and select the data you wish to display from those files. You will also learn how to create visualizations that reveal patterns in the data. This book is based on Chart.js version 2.7.3 and ES2015 JavaScript. By the end of the book, you will be able to create beautiful, efficient and interactive data visualizations for the Web using Chart.js. What you will learn Learn how to create interactive and responsive data visualizations using Chart.js Learn how to create Canvas-based graphics without Canvas programming Create composite charts and configure animated data updates and transitions Efficiently display quantitative information using bar and line charts, scatterplots, and pie charts Learn how to load, parse, and filter external files in JSON and CSV formats Understand the benefits of using a data visualization framework Who this book is for The ideal target audience of this book includes web developers and designers, data journalists, data scientists and artists who wish to create interactive data visualizations...
    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/-/9781789342482/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    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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  • 5
    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
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Data science for marketing analytics (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789952100 , 1789952107
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Marketing ; Data processing ; Marketing research ; Python (Computer program language) ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
    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/-/9781789959413/?ar
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  • 7
    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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  • 8
    Online Resource
    Online Resource
    Machine learning with the Elastic Stack : expert techniques to integrate machine learning with distributed search and analytics (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788471770 , 1788471776
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Information visualization ; Electronic data processing ; Distributed processing ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Leverage Elastic Stack's machine learning features to gain valuable insight from your data Key Features Combine machine learning with the analytic capabilities of Elastic Stack Analyze large volumes of search data and gain actionable insight from them Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn Install the Elastic Stack to use machine learning features Understand how Elastic machine learning is used to detect a variety of anomaly types Apply effective anomaly detection to IT operations and security analytics Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting Combine your created jobs to correlate anomalies of different layers of infrastructure Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
    Note: Description based on online resource; title from title page (Safari, viewed March 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788477543/?ar
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