Business Analytics: The Science of Data-Driven Decision Making U Dinesh Kumar

Introduction to Business Analytics Descriptive Analytics Introduction to Probability Sampling and Estimation Confidence Intervals Hypothesis Testing Analysis of Variance Correlation Analysis Simple Linear Regression Multiple Linear Regression Logistic Regression Decision Trees Forecasting Techniques Clustering Prescriptive Analytics Stochastic Models Six Sigma

The book has 17 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.