Practical business analytics using R and Python : solve business problems using a data-driven approach / Umesh R. Hodeghatta and Umesha Nayak.
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 analyz...
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| Online Access: |
Full Text (via Skillsoft) |
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| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Apress,
[2023]
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| Edition: | Second edition. |
| Subjects: |
| Summary: | 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. |
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| Physical Description: | 1 online resource (716 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781484287545 1484287541 |