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.
Author(s): Matt Taddy
Edition: International Studente Edition
Publisher: Mc Graw Hill
Year: 2023
Language: English
Commentary: practical, modern approach, to data science and business analytics decission making, fors graduation-level of bussines or professionals
Pages: 465
Tags: practical, modern approach, to data science and business analytics decission making
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
