Synopses & Reviews
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, youll get material from Columbia Universitys "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. Youll learn whats 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 youre 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 ONeil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.
Review
O'Neil and Schutt describe the current state of data science byasking a set of top-notch thinkers to describe their jobs and what it is like to do data science. They also prescribe what data sciencecould be as an academic discipline. Their topics include algorithms, logistic regression, time stamps and financial modeling, visualizingdata and detecting fraud, social networks and data journalism, epidemiology, and data leakage and model evaluation. A chapter alsopresent voices of students in O'Neil's course that was the germ of the book.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Synopsis
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 thats so clouded in hype? This insightful book, based on Columbia Universitys 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 youre 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 ONeil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
About the Author
Cathy ONeil 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 Columbias 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
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;