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  • 1
    Online Resource
    Online Resource
    Business analytics using R : a practical approach (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Business ; Data processing ; Management information systems ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
    Note: Includes bibliographical references. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781484225141/?ar
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  • 2
    Online Resource
    Online Resource
    Practical business analytics using R and Python : solve business problems using a data-driven approach (2023)
    New York, NY : Apress
    Details
    ISBN: 9781484287545 , 1484287541
    Language: English
    Pages: 1 online resource (716 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Decision making Data processing ; Business planning Data processing ; R (Computer program language) ; Python (Computer program language) ; Electronic books
    Abstract: This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.
    Note: Includes bibliographical references and index
    URL: lizenzpflichtig
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