Practical business analytics using R and Python : solve business problems using a data-driven approach / Umesh R. Hodeghatta and Umesha Nayak.
Hodeghatta, Umesh R., author.; Nayak, Umesha, author.
2023
HD30.23
Available Online

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Title Practical business analytics using R and Python : solve business problems using a data-driven approach / Umesh R. Hodeghatta and Umesha Nayak.
Author Hodeghatta, Umesh R., author.
Edition Second edition.
ISBN 9781484287545 (electronic bk.)
1484287541 (electronic bk.)
9781484287538
1484287533
DOI https://doi.org/10.1007/978-1-4842-8754-5
Published New York, NY : Apress, [2023]
Language English
Description 1 online resource (716 pages) : illustrations
Item Number 10.1007/978-1-4842-8754-5 doi
Call Number HD30.23
Dewey Decimal Classification 658.4/03
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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Added Author Nayak, Umesha, author.
Available in Other Form Practical Business Analytics Using R and Python
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Section 1: Introduction to Analytics
Chapter 1: Business Analytics Revolution
Chapter 2: Foundations of Business Analytics
Chapter 3: Structured Query Language (SQL) Analytics
Chapter 4: Business Analytics Process
Chapter 5: Exploratory Data Analysis (EDA)
Chapter 6: Evaluating Analytics Model Performance
Section II: Supervised Learning and Predictive Analytics
Chapter 7: Simple Linear Regressions
Chapter 8: Multiple Linear Regressions
Chapter 9: Classification
Chapter 10: Neural Networks
Chapter 11: Logistic Regression
Section III: Time Series Models
Chapter 12: Time Series Forecasting
Section IV: Unsupervised Model and Text Mining
Chapter 13: Cluster Analysis
Chapter 14: Relationship Data Mining
Chapter 15: Mining Text and Text Analytics
Chapter 16: Big Data and Big Data Analytics
Section V: Business Analytics Tools
Chapter 17: R programming for Analytics
Chapter 18: Python Programming for Analytics.

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