# Doing Data Science

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

### Book Details

**Paperback:**406 pages**Publisher:**O’Reilly Media (October 2013)**Language:**English**ISBN-10:**1449358659**ISBN-13:**978-1449358655