Modern Business Analytics https://www.mheducation.com/cover-images/Jpeg_400-high/1264071671.jpeg 1 9781264071678 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.
Modern Business Analytics

Modern Business Analytics

1st Edition
By Matt Taddy and Leslie Hendrix and Matthew Harding
ISBN10: 1264071671
ISBN13: 9781264071678
Copyright: 2023
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ISBN10: 1264071655 | ISBN13: 9781264071654

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ISBN10: 126407168X | ISBN13: 9781264071685

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The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

Program Details

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 

About the Author

Matt Taddy

Leslie Hendrix

Matthew Harding