Business analytics : The science of data-driven decision making / Kumar, U Dinesh
Material type: TextLanguage: English Publisher: Wiley India (P) Ltd 2019Description: 714ISBN: 9788126568772Subject(s): Mathematical Statistics -Business Logistics -- Programming languages (Electronic computers) -- Information retrievalDDC classification: 658.5 KUMItem type | Current location | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
BOOK | Bangaluru Dr. B.R. Ambedkar School of Economics | 658.5 KUM (Browse shelf) | Available | 001156 |
Browsing Bangaluru Dr. B.R. Ambedkar School of Economics shelves Close shelf browser
658.409 SAN Lean in : for graduates / | 658.421 GRE Entrepreneurship : Theory and Practice | 658.45 NAW Business Communication / | 658.5 KUM Business analytics : The science of data-driven decision making / | 658.514 DUE Technology Entrepreneurship: Taking Innovation to the Marketplace | 658.514 EVE Technology Entrepreneurship : Bringing Innovation to the Marketplace : | 658.514 SUB Rethinking innovation : Global perspectives |
The book has 17 chapters and address all components of analytics such as descriptive, predictive and prescriptive analytics. The first few chapters are dedicated to foundations of business analytics. Introduction to business analytics and its components such as descriptive, predictive and prescriptive analytics along with several applications are discussed in Chapter 1. In Chapters 2 to 8, we discuss basic statistical concepts such as descriptive statistics, concept of random variables, discrete and continuous random variables, confidence interval, hypothesis testing, analysis of variance and correlation. Chapters 9 to 13 are dedicated to predictive analytics techniques such as multiple linear regression, logistic regression, decision tree learning and forecasting techniques. Clustering is discussed in Chapter 14. Chapter 15 is dedicated to prescriptive analytics in which concepts such as linear programming, integer programming, and goal programming are discussed. Stochastic models and Six Sigma are discussed in Chapters 16 and 17, respectively.
There are no comments on this title.