Doing Data Science: Straight Talk from the Frontline

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 ...

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Doing Data Science: Straight Talk from the Frontline

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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.

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Editorial Reviews

From the Publisher
"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

 
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Product Details

  • ISBN-13: 9781449358655
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 10/30/2013
  • Edition number: 1
  • Pages: 406
  • Sales rank: 216,205
  • Product dimensions: 5.90 (w) x 8.90 (h) x 1.00 (d)

Meet 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.

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Table of Contents

Dedication

Preface

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

Index

Colophon

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