"I enjoyed Rachel and Cathy's book, it's readable, informative, and like no other book I've read on the topic of statistics or data science."
--Andrew Gelman
Professor of statistics and political science, and director of the Applied Statistics Center at Columbia University
"I got a lot out of Doing Data Science, finding the chapter organization on business problem specification, analytics formulation, data access/wrangling, and computer code to be very helpful in understanding DS solutions."
--Steve Miller
Co-founder, OpenBI, LLC, a Chicago-based business intelligence services firm
Dedication
Preface
Chapter 1: Introduction: What Is Data Science?
Chapter 2: Statistical Inference, Exploratory Data Analysis, and the Data Science Process
Chapter 3: Algorithms
Chapter 4: Spam Filters, Naive Bayes, and Wrangling
Chapter 5: Logistic Regression
Chapter 6: Time Stamps and Financial Modeling
Chapter 7: Extracting Meaning from Data
Chapter 8: Recommendation Engines: Building a User-Facing Data Product at Scale
Chapter 9: Data Visualization and Fraud Detection
Chapter 10: Social Networks and Data Journalism
Chapter 11: Causality
Chapter 12: Epidemiology
Chapter 13: Lessons Learned from Data Competitions: Data Leakage and Model Evaluation
Chapter 14: Data Engineering: MapReduce, Pregel, and Hadoop
Chapter 15: The Students Speak
Chapter 16: Next-Generation Data Scientists, Hubris, and Ethics
Index
Colophon