Buy new:
-22% $42.66
FREE delivery Thursday, May 16
Ships from: Amazon
Sold by: sameday
$42.66 with 22 percent savings
List Price: $54.99

The List Price is the suggested retail price of a new product as provided by a manufacturer, supplier, or seller. Except for books, Amazon will display a List Price if the product was purchased by customers on Amazon or offered by other retailers at or above the List Price in at least the past 90 days. List prices may not necessarily reflect the product's prevailing market price.
Learn more
FREE Returns
FREE delivery Thursday, May 16
Or fastest delivery Wednesday, May 15. Order within 9 hrs 33 mins
Only 1 left in stock - order soon.
$$42.66 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$42.66
Subtotal
Initial payment breakdown
Shipping cost, delivery date, and order total (including tax) shown at checkout.
Ships from
Amazon
Ships from
Amazon
Sold by
sameday
Sold by
sameday
Returns
Eligible for Return, Refund or Replacement within 30 days of receipt
Eligible for Return, Refund or Replacement within 30 days of receipt
This item can be returned in its original condition for a full refund or replacement within 30 days of receipt.
Read full return policy
Returns
Eligible for Return, Refund or Replacement within 30 days of receipt
This item can be returned in its original condition for a full refund or replacement within 30 days of receipt.
Read full return policy
Payment
Secure transaction
Your transaction is secure
We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more
Payment
Secure transaction
We work hard to protect your security and privacy. Our payment security system encrypts your information during transmission. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Learn more
$12.64
Get Fast, Free Shipping with Amazon Prime FREE Returns
FREE delivery Friday, May 17 on orders shipped by Amazon over $35
Only 1 left in stock - order soon.
$$42.66 () Includes selected options. Includes initial monthly payment and selected options. Details
Price
Subtotal
$$42.66
Subtotal
Initial payment breakdown
Shipping cost, delivery date, and order total (including tax) shown at checkout.
Access codes and supplements are not guaranteed with used items.
Amazon book clubs early access
Loading your book clubs
There was a problem loading your book clubs. Please try again.
Not in a club? Learn more
Amazon book clubs early access

Join or create book clubs

Choose books together

Track your books
Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free.
Explore Amazon Book Clubs
Kindle app logo image

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required.

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

QR code to download the Kindle App

Follow the authors

Cathy O'Neil
+ Follow
Rachel Schutt
+ Follow
See all
Something went wrong. Please try your request again later.

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

by Cathy O'Neil (Author), Rachel Schutt (Author)
4.4 4.4 out of 5 stars 228 ratings
See all formats and editions
{"desktop_buybox_group_1":[{"displayPrice":"$42.66","priceAmount":42.66,"currencySymbol":"$","integerValue":"42","decimalSeparator":".","fractionalValue":"66","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"UUl%2Bfo%2BRxZ8qCCjV1MqgTY1SP83pPpbRfMq6DP%2FVuN6JVEXqTsPjvEImAw%2F7WLLdoVOir%2Fmzzx%2FvfRD5j9lethrz48htgR7lW4lbbLpgc5Oi9DSlTCulMv6ZylVyEbVD0lBsuHJuHjDTXk9W3HTh4qQfTADbZllFhHcw9GKYLZJlG1jnpvx9R1cUgH%2FmexlU","locale":"en-US","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}, {"displayPrice":"$12.64","priceAmount":12.64,"currencySymbol":"$","integerValue":"12","decimalSeparator":".","fractionalValue":"64","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"UUl%2Bfo%2BRxZ8qCCjV1MqgTY1SP83pPpbRU6LHtePs6apZ4PbpoTrUoXdNTv1bukAy9jEbocgtiT8hTL%2Fcq0JyoiOydldAo%2BmuMkXrME3G31y3TqSp7MMZdO1GkSZ5p4duBv2j6ofpB6AdSMeRd7wa%2BhBcQClm38A4DG%2F%2BgzY7cHoKdRXaaHsbMUBcFvyfEry0","locale":"en-US","buyingOptionType":"USED","aapiBuyingOptionIndex":1}]}

Purchase options and add-ons

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.

Read more Read less
Previous page
  1. ISBN-10
    1449358659
  2. ISBN-13
    978-1449358655
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    December 3, 2013
  6. Language
    English
  7. Dimensions
    6 x 0.78 x 9 inches
  8. Print length
    405 pages
  9. See all details
Next page
Amazon First Reads | Editors' picks at exclusive prices

Frequently bought together

Doing Data Science: Straight Talk from the Frontline
$42.66
Get it as soon as Thursday, May 16
Only 1 left in stock - order soon.
Sold by sameday and ships from Amazon Fulfillment.
+
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
$29.49
Get it May 16 - 21
Only 1 left in stock - order soon.
Ships from and sold by sweethomeliquid2.
+
Data Science from Scratch: First Principles with Python
$43.94
Get it as soon as Wednesday, May 15
In Stock
Ships from and sold by Amazon.com.
Total price:
To see our price, add these items to your cart.
Details
Added to Cart
Some of these items ship sooner than the others.
Show details Hide details
Choose items to buy together.

From the brand

Previous page
  2. Data Science from Scratch: First Principles with Python
    Python Data Science Handbook
    Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Proba...
    Data Science: The Hard Parts: Techniques for Excelling at Data Science

    Explore more Data Science

    Visit the Store

  3. Oreilly

    Sharing the knowledge of experts

    O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Next page

From the Publisher

Data Science, Business Data Science from Scratch Data Science, big data R, Data Science, r programming Data Science at the Command Line Python, Data Science
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
1,292
4.4 out of 5 stars
708
4.4 out of 5 stars
228
4.7 out of 5 stars
1,594
4.5 out of 5 stars
27
4.6 out of 5 stars
674
Price $32.47 $43.94 $42.66 $25.47 $39.49 $74.81
More Data Science from O'Reilly Media 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

Editorial Reviews

Review

"Every once in a while a single book comes to crystallize a new discipline. If books still have this power in the era of electronic media, "Doing Data Science: Straight Talk from the Frontline" by Rachel Schutt and Cathy O'Neil: O'Reilly, 2013 might just be the book that defines data science." -- Joseph RickertRevolutions Blog

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 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.

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (December 3, 2013)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 405 pages
  • ISBN-10 ‏ : ‎ 1449358659
  • ISBN-13 ‏ : ‎ 978-1449358655
  • Item Weight ‏ : ‎ 1.2 pounds
  • Dimensions ‏ : ‎ 6 x 0.78 x 9 inches
  • Customer Reviews:
    4.4 4.4 out of 5 stars 228 ratings

About the authors

Follow authors to get new release updates, plus improved recommendations.
Previous page
  1. Cathy O'Neil

    Cathy O'Neil

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    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

    Brief content visible, double tap to read full content.
    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
Next page

Customer reviews

4.4 out of 5 stars
4.4 out of 5
228 global ratings

Top reviews from the United States

I Teach Typing
5.0 out of 5 stars Beautiful with excellent references but the code needs work
Reviewed in the United States on March 7, 2014
Verified Purchase
This is a beautiful, thoughtful survey with excellent references. I am an academic data scientist with nearly 20 years experience and I wanted a book to offer my students who are starting in the field. This is it.

The "difficulty" with data science is in the breadth of skills that are needed. Because data scientists need training in art, communication, statistics, and programming nobody is prepared to handle all the tasks and the neophyte (and expert) will need to fill in around their weaknesses. This book does a brilliant job of working around that issue. The writing is superb for a beginning to intermediate reader and the graphics and aside boxes are engaging. More importantly. the references are plentiful and spot on. In the areas I know well the authors suggest the things I recommend and where I am weak the recommendations have proven interesting.

While this is a broad survey, there is some depth here. There are formulas throughout but the book does not get bogged down in proofs and derivations. There are programs written in R code scattered throughout. The code is nicely commented but there is not a deep dive into how it words. So, the reader who knows some R will learn a few new tricks but it does not interrupt the flow of the book.

A reader who types the R code will run into problems. Clearly the authors/editors did not attempt to run the code after the type setter mangled it. For example, on page 39 there is a line which begins with a + and that character needed to be on the previous line. In other places, (like page 49) functions are invoked (count) but the authors have not included the commands to make the functions available (in this case library(plyr)). Sadly there does not seem to be an errata for the book and these will be major headaches or show stoppers for novices.

While this book could be improved with a code supplement on the web (including the code to make all the graphics, complete solutions to the example/problems and an errata), this is a wonderful buy for readers of all levels.
9 people found this helpful
Helpful
 Report
Doctor Integral
4.0 out of 5 stars Great Introduction to the Field!
Reviewed in the United States on December 27, 2014
Verified Purchase
I purchased and read this book because I was interested in learning about what data scientists "do" on a day-to-day basis, and the book definitely delivers in that respect. It is very up-front in stating that it does not cover many of the details (e.g., requisite math/statistics background, coding in R, etc.), but it provides numerous references for learning this material for those who wish to take a deeper dive. It provides perspectives from a number of experts in the field, along with their associated domains of expertise, and hence appears to give a good sampling of the different backgrounds and personalities of self-labelled data scientists.

From a pedagogical perspective, I think it's a valiant attempt to create a class that's not so "academic." The book is a compendium of individual lectures that were the basis of a data science class at Columbia University, and the corresponding assignments were aimed at giving students a flavor of real-world data science problems (where data is messy, specific questions regarding outcomes are not-well-formed, etc.), which I think is an amazingly valuable experience to give students perspective on what the field is about. But I wonder if the course went a little *too* far into "the real world is not pretty" aspect of things. The students wrote a chapter at the end of the book where they criticize a standard academic problem that comes from a statistical learning textbook and that, in my opinion, is a negative outcome. While elegant mathematical theories do not describe the complexities of the real world, understanding the subtleties of algorithms is an important part of any scientific field, and to discount that is a disservice to the students. Too much depth and not enough breadth is bad in the real world, but so is not having *enough* depth. On the whole, though, I really applaud the approach of the authors at building a "real-world" class.

One of the things that book has left me pondering is whether there is a clear distinction between data *science* and data *engineering*, and whether it is possible to have clear roles for people who are predominately scientists than those who are predominately engineers. A few of the guest lecturers claim that 90% of the work of a data scientist is organizing the data, but I can't help but wonder if that's the bias of the individual speakers---that they *like* this component of the work and, therefore, focus on that more, rather than it being an absolute must to spend 90% of your time working on data structuring. I am sure that if the authors were to read this comment, they would think that I am missing one of their major points (namely, that one can make a lot of mistakes in data leakage and that like from poor data structures, hence making this an integral part of the process). But I think there's a certain personality type that really loves working on the actually processing component who would otherwise be turned off my too much data structuring if it really composed 90% of the work. And harnessing the skills from those types of people is undoubtedly invaluable. So I'm really wondering if many companies are able to break up roles where some people (the scientists) focus more on the processing part, while others (the engineers) focus more on the structuring part.
4 people found this helpful
Helpful
 Report
Cesar A. Brea
5.0 out of 5 stars A healthy blend of competence and humility
Reviewed in the United States on October 13, 2014
Verified Purchase
I make my living working with businesses to help them build their analytic capabilities in sales and marketing, by working on real opportunities and generalizing lessons from specific experiences and results. Business sort of demands that over time you narrow problem-defining frameworks, problem-solving techniques, and problem-processing operations to what you can scale and consistently deliver. But what I have learned is that if you push these past use as points of departure, and force them as points of arrival, results and learning suffer. (As they say, if you have a hammer, then everything looks like, etc. etc.) This is especially true as things get more complex on all these fronts. What I appreciated about this book was that even as it is an excellent survey of specific approaches and techniques at the coal-face of their application (there are code samples here, people!), it's also a thoughtful exposition of the limits of these and of the pitfalls they present. You might be tempted to skip Chapters 15 and 16 if you're just looking to RTFM, but you shouldn't; for me they were the real meat of the book. Maybe it's confirmation bias at work, but it was reassuring to hear such a humble tone coming at the end of such a competent treatment of different data science challenges. Doing this work very occasionally brings triumph and much more often despair, so it helps to have reminders from seasoned pros that what really matters is just trying, steadily, to get better at your craft.
3 people found this helpful
Helpful
 Report

Top reviews from other countries

Translate all reviews to English
M
5.0 out of 5 stars The Only Book You'll Need
Reviewed in the United Kingdom on December 22, 2020
Verified Purchase
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
 Report
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 April 9, 2019
Verified Purchase
Para mim, o livro foi perfeito. Linguagem clara e toca os itens principais...
Report
peter kremer
5.0 out of 5 stars Insightful and well written
Reviewed in Canada on September 24, 2017
Verified Purchase
Covers many topics and gives examples. Authors tie subjects to their own work experiences.
One person found this helpful
 Report
Sridhar Chetan
5.0 out of 5 stars Great book to start DataScience
Reviewed in India on July 23, 2019
Verified Purchase
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
 Report
Ignacio Rodera Tobal
5.0 out of 5 stars libro de cabecera de DataScience
Reviewed in Spain on June 23, 2017
Verified Purchase
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
 Report