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Doing Data Science Paperback – 18 October 2013

by Rachel Schutt (Author), Cathy O?neil (Contributor)
4.4 out of 5 stars 202
Edition: 1st
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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, you'll get material from Columbia University's "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. You'll learn what's 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 you're 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 O'Neil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.

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  1. ISBN-10
    1449358659
  2. ISBN-13
    978-1449358655
  3. Edition
    1st
  4. Publisher
    O'Reilly Media, Inc, USA
  5. Publication date
    18 October 2013
  6. Language
    English
  7. Dimensions
    15.24 x 1.98 x 22.86 cm
  8. Print length
    300 pages
  9. See all details
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From the Publisher

Data Science for Business What You Need to Know about Data Mining and Data-Analytic Thinking Data Science from Scratch First Principles with Python Doing Data Science Straight Talk from the Frontline R for Data Science Visualize, Model, Transform, Tidy, and Import Data Data Science at the Command Line Facing the Future with Time-Tested Tools Python Data Science Handbook Tools and Techniques for Developers
Data Science for Business Data Science from Scratch Doing Data Science R for Data Science Data Science at the Command Line Python Data Science Handbook
Customer Reviews
4.5 out of 5 stars
1,324
4.3 out of 5 stars
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4.4 out of 5 stars
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4.7 out of 5 stars
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4.1 out of 5 stars
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4.5 out of 5 stars
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Price $62.45 $119.99 $70.20 $64.00 $80.84 $39.00
What You Need to Know about Data Mining and Data-Analytic Thinking First Principles with Python Straight Talk from the Frontline Visualize, Model, Transform, Tidy, and Import Data Facing the Future with Time-Tested Tools Tools and Techniques for Developers
Pages 414 330 408 250 212 500

Product description

Review

"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

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 a Senior Statistician at Google Research in the New York office and adjunct assistant professor at Columbia University. She earned a PhD from Columbia University in statistics, and masters degrees in mathematics and operations research from the Courant Institute and Stanford University, respectively. Her statistical research interests include modeling and analyzing social networks, epidemiology, hierarchical modeling and Bayesian statistics. Her education-related research interests include curriculum design.

Product details

  • Publisher ‏ : ‎ O'Reilly Media, Inc, USA; 1st edition (18 October 2013)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 300 pages
  • ISBN-10 ‏ : ‎ 1449358659
  • ISBN-13 ‏ : ‎ 978-1449358655
  • Dimensions ‏ : ‎ 15.24 x 1.98 x 22.86 cm
  • Customer Reviews:
    4.4 out of 5 stars 202

About the authors

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  1. Cathy O'Neil

    Cathy O'Neil

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    I am a mathematician turned quant turned algorithmic auditor living in Cambridge, MA.

    See more on the author's page
  2. Rachel Schutt

    Rachel Schutt

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    Full content visible, double tap to read brief content.

    Rachel Schutt is a Managing Director at BlackRock where she is the Head of BlackRock AI Labs. She held previous roles as the Chief Data Scientist at News Corp and at Google Research in the machine learning and statistics group. Rachel was named a World Economic Forum Young Global Leader in 2015. She earned her PhD in Statistics from Columbia University. She lives in Brooklyn, NY.

    See more on the author's page
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4.4 out of 5 stars
4.4 out of 5
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Top review from Australia

D. Hawthorne
4.0 out of 5 stars Not for the general reader
Reviewed in Australia on 27 August 2018
Not for the general reader.

This is a somewhat difficult book about an emerging discipline of Data Science by a pre-eminent practitioner. But it is a necessary book to explain what this discipline encompasses. It is still too early to be absorbed into popular culture.

This is really a text book which requires the reader to do the exercises so that the reader can grasp the topics. This is what makes the book difficult for the general reader. One has to be sufficiently competent enough with computers, programming, and statistics in order to appreciate the evolution of Data Science.

Despite its difficulty, this book is a necessary introduction for the general public to understand the threat to democratic society from the misuse of Data Science. O'Neil explains this further in her call-to-arms ( Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy ).

Unfortunately, public culture still regards computers as magic. Say the right words, and the answer magically appears. Data Science is far more than just using computers -- Data Science involves the collection, analysis, cleaning, and presentation of enormous amounts of data.
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Top reviews from other countries

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M
5.0 out of 5 stars The Only Book You'll Need
Reviewed in the United Kingdom on 22 December 2020
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There are many books out there on Data Science, Machine Learning and Data Analytics. I can think of none that understand the subject so well they make it easy for the reader to understand. Einstein once said if you can't explain something in simple terms you don't really understand it. Well O'Neil and Schutt pass the Einstein test. Other books I've read on the subject are either written by a computer scientist who is light on maths and so they leave the maths opaque. Or they are a mathematicians who are strong on stats and light on programming. O'Neil and Schutt are strong in both and as the title would suggest its about doing Data Science rather than the academic theory of Data Science and Machine Learning. Another thing I love about this book is it debunks the jargon. Very refreshing. Whoever you are at whatever level this is the last word in Data Science. I would consider it perfect if it included code for Python too. It doesn't it's R and the R code requires updating since publication but that's easily done if you are interested. Can't recommend this book more highly. The essential read for the subject.
2 people found this helpful
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Gian
5.0 out of 5 stars Excelente, deixa claro os próximos caminhos a serem seguidos por um cientista de dados
Reviewed in Brazil on 9 April 2019
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Para mim, o livro foi perfeito. Linguagem clara e toca os itens principais...
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peter kremer
5.0 out of 5 stars Insightful and well written
Reviewed in Canada on 24 September 2017
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Covers many topics and gives examples. Authors tie subjects to their own work experiences.
One person found this helpful
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Sridhar Chetan
5.0 out of 5 stars Great book to start DataScience
Reviewed in India on 23 July 2019
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Good one to start with on DS .. Various concepts and Algos presented in a easy to understand fashions. Mostly Coded in R i would hv expected python which is what we use mostly today.
3 people found this helpful
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Ignacio Rodera Tobal
5.0 out of 5 stars libro de cabecera de DataScience
Reviewed in Spain on 23 June 2017
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El libro altamente recomendable, si te quieres adentrar en el mundo del DataScience
Un libro que no puede faltar en tu estantería

El envío perfecto
One person found this helpful
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