Doing Data Science: Straight Talk from the Frontline

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

• Statistical inference, exploratory data analysis, and the data science process
• Algorithms
• Spam filters, Naive Bayes, and data wrangling
• Logistic regression
• Financial modeling
• Recommendation engines and causality
• Data visualization
• Social networks and data journalism
• Data engineering, MapReduce, Pregel, and Hadoop

Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Table of Contents

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