Modern Business Analytics ISE

Chapter 1: Regression 
Chapter 2: Uncertainty Quantification 
Chapter 3: Regularization and Selection 
Chapter 4: Classification 
Chapter 5: Causal Inference with Experiments 
Chapter 6: Causal Inference with Controls 
Chapter 7: Trees and Forests 
Chapter 8: Factor Models 
Chapter 9: Text as Data 
Chapter 10: Deep Learning 
Appendix: R Primer 

Written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science graduate of University of Chicago and Amazon Chief Economist. This new higher-ed text takes a practical, modern approach to data science and business analytics for the graduate-level business analytics student or professional. It takes a learn-by-doing approach, with real data analysis examples that explain the "why", rather than the "what" in the decision-making discussions. It uses R as the primary technology throughout the text and includes an end-of-chapter reference to the basic R recipes in each chapter. The text uses tools from economics and statistics in combination with Machine Learning Techniques to create a platform for using data to make decisions.

The Connect product that supports the text includes Interactive Activities that have students explore content more deeply, Excel activities like Integrated Excel & Applying Excel, and a Prep Course that helps students refresh on fundamental pre-requisite knowledge they need to know prior to this course.