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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 227 ratings
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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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  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
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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

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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 227 ratings

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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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    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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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
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Doctor Integral
4.0 out of 5 stars Great Introduction to the Field!
Reviewed in the United States on December 27, 2014
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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
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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
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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
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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 April 9, 2019
Verified Purchase
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 September 24, 2017
Verified Purchase
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 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
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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
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