Instructor Resources (Data)
Chapter 2 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17Reader Resources
Chapter 1 Chapter 2 Excellent book...
Incredibly a good book....a good combination of economics ,statistics ,econometrics and finance ... Written a lucid fashion..easy to comprehend..delivered to me in a extremely good condition..
Practical and concept oriented
very good concepts, not for on the go read, you need to have pen and paper to understand he book. but you will get very good concept with variety business cases, real examples
Business Analytics: The Science of Data - Driven Decision Making
Overview
The book has 18 chapters and addresses 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. Chapter 18 talks about Six Sigma.
Authors
Professor U Dinesh Kumar
Professor U Dinesh Kumar, is a professor of Decision Sciences and Chair of Excellence at the Indian Institute of Management Bangalore. Dr Dinesh Kumar has over 25 years of teaching and research experience. Prior to joining IIM Bangalore, Dr Dinesh Kumar has worked at several reputed Institutes across the world including Stevens Institute of Technology, USA; University of Exeter, UK; University of Toronto, Canada; Federal Institute of Technology (ETH), Zurich, Switzerland; Queensland University of Technology, Australia; Australian National University, Australia and the Indian Institute of Management Calcutta.
Recognised as one of the Top 10 Most Prominent Analytic Academicians in India, his main research and teaching interests are Business Analytics and Artificial Intelligence, and he has published 38 case studies at the Harvard Business Publishing on data science and machine learning, Nine of his case studies are best-sellers at the Harvard Business Publishing case portal. He has authored more than 70 research articles, and two books. His books, Business Analytics – The Science of Data-Driven Decision-Making and Machine Learning using Python are Amazon India best-sellers.
Dr Dinesh Kumar has conducted training program on Analytics for several companies such as Accenture, Aditya Birla Group, Ashok Leyland, Asian Paints, Bank of America, Blue Ocean Market Intelligence, Cisco, Fidelity, Hindustan Aeronautics Limited, Honeywell, Infosys, ITC Info Tech, Madya Pradesh Agency for Promotion of Information Technology (MAPIT), National Academy for Defence Production, Ocwen financial Services, SONY and so on. Dr Dinesh Kumar conducts corporate training programme in Analytics and trained more than 1000 professionals in the field of analytics.
Excellent PG level Text Book
The book takes readers through easy and difficult techniques of Analytics. Theory and practice is covered in equal measure. Case studies are provided with data so that one can work through solutions and application. This book is quite appropriate as text book at PG level.
Excellent book for Business Analytics
This book covers most of the Analytical concepts, Statistical concepts, Machine learning techniques, Regression Analysis, etc. Happy to find this book and with the content.
