Doing Data Science: Straight Talk from the Frontline / Edition 1

Doing Data Science: Straight Talk from the Frontline / Edition 1

by Cathy O'Neil, Rachel Schutt
     
 

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ISBN-10: 1449358659

ISBN-13: 9781449358655

Pub. Date: 10/30/2013

Publisher: O'Reilly Media, Incorporated

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

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:
408
Sales rank:
121,342
Product dimensions:
5.90(w) x 8.90(h) x 1.00(d)

Related Subjects

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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