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Doing data science $b.

Author: Rachel Schutt; Cathy O'Neil
Publisher: Sebastopol, Calif. : O'Reilly, [2014]
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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 is so clouded in hype? This book 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  Read more...
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Document Type: Book
All Authors / Contributors: Rachel Schutt; Cathy O'Neil
ISBN: 9781449358655 1449358659
OCLC Number: 1012673202
Notes: Includes index.
Subtitle on cover: straight talk from the frontline.
Description: xxiv, 375 pages : illustrations ; 23 cm
Contents: What is data science? --
Statistical inference, exploratory data analysis, and the data science process --
Algorithms --
Spam filters, naive bayes, and wrangling --
Logistic regression --
Time stamps and financial modeling --
Extracting meaning from data --
Recommendation engines : building a user-facing data product at scale --
Data visualization and fraud detection --
Social networks and data journalism --
Causality --
Epidemiology --
Lessons learned from data competitions : data leakage and model evaluation --
Data engineering : MapReduce, Pregel, and Hadoop --
The students speak --
Next-generation data scientists, hubris, and ethics.
Other Titles: Straight talk from the frontline
Responsibility: Cathy O'Neil and Rachel Schutt.

Abstract:

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 is so clouded in hype? This book 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 are 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.

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