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.
- Statistical inference, exploratory data analysis, and the data science process
- 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.
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About the Author
Cathy O’Neil earned a Ph.D. in math from Harvard, was postdoc at the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then chucked it and switched over to the private sector. She worked as a quant for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. She is currently a data scientist on the New York start-up scene, writes a blog at mathbabe.org, and is involved with Occupy Wall Street.
Rachel Schutt is the Senior Vice President for Data Science at News Corp. She earned a PhD in Statistics from Columbia University, and was a statistician at Google Research for several years. She is an adjunct professor in Columbia’s Department of Statistics and a founding member of the Education Committee for the Institute for Data Sciences and Engineering at Columbia. She holds several pending patents based on her work at Google, where she helped build user-facing products by prototyping algorithms and building models to understand user behavior. She has a master's degree in mathematics from NYU, and a master's degree in Engineering-Economic Systems and Operations Research from Stanford University. Her undergraduate degree is in Honors Mathematics from the University of Michigan.
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