Doing Data Science: Straight Talk from the Frontline

by Cathy O'Neil, Rachel Schutt

Overview

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.

Product Details

ISBN-13:	9781449358655
Publisher:	O'Reilly Media, Incorporated
Publication date:	10/30/2013
Pages:	405
Sales rank:	1,149,779
Product dimensions:	5.90(w) x 8.90(h) x 1.00(d)

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 Universityof Michigan.

Table of Contents

Dedication; Preface; Motivation; Origins of the Class; Origins of the Book; What to Expect from This Book; How This Book Is Organized; How to Read This Book; How Code Is Used in This Book; Who This Book Is For; Prerequisites; Supplemental Reading; About the Contributors; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Chapter 1: Introduction: What Is Data Science?; 1.1 Big Data and Data Science Hype; 1.2 Getting Past the Hype; 1.3 Why Now?; 1.4 The Current Landscape (with a Little History); 1.5 A Data Science Profile; 1.6 Thought Experiment: Meta-Definition; 1.7 OK, So What Is a Data Scientist, Really?; Chapter 2: Statistical Inference, Exploratory Data Analysis, and the Data Science Process; 2.1 Statistical Thinking in the Age of Big Data; 2.2 Exploratory Data Analysis; 2.3 The Data Science Process; 2.4 Thought Experiment: How Would You Simulate Chaos?; 2.5 Case Study: RealDirect; Chapter 3: Algorithms; 3.1 Machine Learning Algorithms; 3.2 Three Basic Algorithms; 3.3 Exercise: Basic Machine Learning Algorithms; 3.4 Summing It All Up; 3.5 Thought Experiment: Automated Statistician; Chapter 4: Spam Filters, Naive Bayes, and Wrangling; 4.1 Thought Experiment: Learning by Example; 4.2 Naive Bayes; 4.3 Fancy It Up: Laplace Smoothing; 4.4 Comparing Naive Bayes to k-NN; 4.5 Sample Code in bash; 4.6 Scraping the Web: APIs and Other Tools; 4.7 Jake’s Exercise: Naive Bayes for Article Classification; Chapter 5: Logistic Regression; 5.1 Thought Experiments; 5.2 Classifiers; 5.3 M6D Logistic Regression Case Study; 5.4 Media 6 Degrees Exercise; Chapter 6: Time Stamps and Financial Modeling; 6.1 Kyle Teague and GetGlue; 6.2 Timestamps; 6.3 Cathy O’Neil; 6.4 Thought Experiment; 6.5 Financial Modeling; 6.6 Exercise: GetGlue and Timestamped Event Data; Chapter 7: Extracting Meaning from Data; 7.1 William Cukierski; 7.2 The Kaggle Model; 7.3 Thought Experiment: What Are the Ethical Implications of a Robo-Grader?; 7.4 Feature Selection; 7.5 David Huffaker: Google’s Hybrid Approach to Social Research; Chapter 8: Recommendation Engines: Building a User-Facing Data Product at Scale; 8.1 A Real-World Recommendation Engine; 8.2 Thought Experiment: Filter Bubbles; 8.3 Exercise: Build Your Own Recommendation System; Chapter 9: Data Visualization and Fraud Detection; 9.1 Data Visualization History; 9.2 What Is Data Science, Redux?; 9.3 A Sample of Data Visualization Projects; 9.4 Mark’s Data Visualization Projects; 9.5 Data Science and Risk; 9.6 Data Visualization at Square; 9.7 Ian’s Thought Experiment; 9.8 Data Visualization for the Rest of Us; Chapter 10: Social Networks and Data Journalism; 10.1 Social Network Analysis at Morning Analytics; 10.2 Social Network Analysis; 10.3 Terminology from Social Networks; 10.4 Thought Experiment; 10.5 Morningside Analytics; 10.6 More Background on Social Network Analysis from a Statistical Point of View; 10.7 Data Journalism; Chapter 11: Causality; 11.1 Correlation Doesn’t Imply Causation; 11.2 OK Cupid’s Attempt; 11.3 The Gold Standard: Randomized Clinical Trials; 11.4 A/B Tests; 11.5 Second Best: Observational Studies; 11.6 Three Pieces of Advice; Chapter 12: Epidemiology; 12.1 Madigan’s Background; 12.2 Thought Experiment; 12.3 Modern Academic Statistics; 12.4 Medical Literature and Observational Studies; 12.5 Stratification Does Not Solve the Confounder Problem; 12.6 Is There a Better Way?; 12.7 Research Experiment (Observational Medical Outcomes Partnership); 12.8 Closing Thought Experiment; Chapter 13: Lessons Learned from Data Competitions: Data Leakage and Model Evaluation; 13.1 Claudia’s Data Scientist Profile; 13.2 Data Mining Competitions; 13.3 How to Be a Good Modeler; 13.4 Data Leakage; 13.5 How to Avoid Leakage; 13.6 Evaluating Models; 13.7 Choosing an Algorithm; 13.8 A Final Example; 13.9 Parting Thoughts; Chapter 14: Data Engineering: MapReduce, Pregel, and Hadoop; 14.1 About David Crawshaw; 14.2 Thought Experiment; 14.3 MapReduce; 14.4 Word Frequency Problem; 14.5 Other Examples of MapReduce; 14.6 Pregel; 14.7 About Josh Wills; 14.8 Thought Experiment; 14.9 On Being a Data Scientist; 14.10 Economic Interlude: Hadoop; 14.11 Back to Josh: Workflow; 14.12 So How to Get Started with Hadoop?; Chapter 15: The Students Speak; 15.1 Process Thinking; 15.2 Naive No Longer; 15.3 Helping Hands; 15.4 Your Mileage May Vary; 15.5 Bridging Tunnels; 15.6 Some of Our Work; Chapter 16: Next-Generation Data Scientists, Hubris, and Ethics; 16.1 What Just Happened?; 16.2 What Is Data Science (Again)?; 16.3 What Are Next-Gen Data Scientists?; 16.4 Being an Ethical Data Scientist; 16.5 Career Advice; Index; Colophon;

Editorial Reviews

"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
 

From the Publisher