Doing Data Science

Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you'll get material from Columbia University's "Introduction to Data Science" class in an easy-to-follow format. Each chapter-long lecture features a guest data scientist from a prominent company such as Google, Microsoft, or eBay teaching new algorithms, methods, or models by sharing case studies and actual code they use. You'll learn what's involved in the lives of data scientists and be able to use the techniques they present. Guest lectures focus on topics such as: Machine learning and data mining algorithms Statistical models and methods Prediction vs. description Exploratory data analysis Communication and visualization Data processing Big data Programming Ethics Asking good questions If you're familiar with linear algebra, probability and statistics, and have some programming experience, this book will get you started with data science. Doing Data Science is collaboration between course instructor Rachel Schutt (also employed by Google) and data science consultant Cathy O'Neil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.

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 a Senior Statistician at Google Research in the New York office and adjunct assistant professor at Columbia University. She earned a PhD from Columbia University in statistics, and masters degrees in mathematics and operations research from the Courant Institute and Stanford University, respectively. Her statistical research interests include modeling and analyzing social networks, epidemiology, hierarchical modeling and Bayesian statistics. Her education-related research interests include curriculum design.

Review :
"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