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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.
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
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