Modern business analytics : practical data science for decision making

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. Cover Modern Business Analytics About the Authors Brief Contents Contents Preface Guided Tour Acknowledgments Chapter 1: Regression Linear Regression Logistic Regression Likelihood and Deviance Time Series Spatial Data Chapter 2: Uncertainty Quantification Frequentist Uncertainty False Discovery Rate Control The Bootstrap More on Bootstrap Sampling Bayesian Inference Chapter 3: Regularization and Selection Out-of-Sample Performance Building Candidate Models Model Selection Uncertainty Quantification for the Lasso Chapter 4: Classification Nearest Neighbors Probability, Cost, and Classification Classification via Regression Multinomial Logistic Regression Chapter 5: Causal Inference with Experiments Notation for Causal Inference Randomized Controlled Trials Regression Adjustment Regression Discontinuity Designs Instrumental Variables Design of Experiments Chapter 6: Causal Inference with Controls Conditional Ignorability Double Machine Learning Heterogeneous Treatment Effects Using Time Series as Controls Chapter 7: Trees and Forests Decision Trees Random Forests Causal Inference with Random Forests Distributed Computing for Random Forests Chapter 8: Factor Models Clustering Factor Models and PCA Factor Regression Partial Least Squares Chapter 9: Text as Data Tokenization Text Regression Topic Models Word Embedding Chapter 10: Deep Learning The Ingredients of Deep Learning Working with Deep Learning Frameworks Stochastic Gradient Descent The State of the Art Intelligent Automation Appendix: R Primer Getting Started with R Working with Data Advanced Topics for Functions Organizing Code, Saving Work, and Creating Reports Bibliography Glossary Acronyms Index 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 (0818), 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 Practical Data Science for Decision Making has crossed the boundaries and created something truly interdisciplinary. "The practice of data analytics is changing and modernizing. Innovations in computation and machine learning are creating new opportunities for the data analyst: exposing previously unexplored data to scientific analysis, scaling tasks through automation, and allowing deeper and more accurate modeling"-- Provided by publisher