Modern Business Analytics

by Matthew C. Harding, Leslie Hendrix, Matt Taddy

Overview

This higher-ed text takes a practical, modern approach to data science and business analytics for the analytics student or professional. It helps them learn by doing, with real data analysis examples that explain the "why", rather than the "what" in 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. It is written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science, former professor at the University of Chicago (‘08–‘18), and Vice President at Amazon, alongside his esteemed colleagues, Dr. Leslie Hendrix, associate professor at the Darla Moore School of Business at the University of South Carolina, and Dr. Matthew C. Harding, professor of economics and statistics at the University of California, Irvine. With their collective authorship, Modern Business Analytics: Practical Data Science for Decision Making has crossed the boundaries and created something truly interdisciplinary.

Product Details

ISBN-13:	9781264071678
Publisher:	McGraw Hill LLC
Publication date:	03/07/2022
Pages:	464
Product dimensions:	8.10(w) x 10.10(h) x 0.90(d)
Age Range:	18 Years

About the Author

Matthew C. Harding is a professor of economics and statistics at the University of California, Irvine. He holds a PhD from MIT and an M.Phil. from Oxford University. Dr. Harding conducts research on econometrics, consumer finance, health policy, and energy economics and has published widely in leading academic journals. He is the founder of Ecometricx, LLC, a big data and machine learning consulting company, and cofounder of FASTlab.global Institute, a nonprofit focusing on education and evidence-based policies in the areas of fair access and sustainable technologies.

Leslie Hendrix is a clinical associate professor in the Darla Moore School of Business at the University of South Carolina. She received her PhD in statistics in 2011 and a BS in mathematics in 2005 from the University of South Carolina. She has received two university-wide teaching awards for her work in teaching business analytics and statistics courses and is active in the research and teaching communities for analytics. She was instrumental in founding the Moore School’s newly formed Data Lab and currently serves as the assistant director.

Table of Contents

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