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Doing Data Science: Straight Talk from the Frontline
PDF Download Doing Data Science: Straight Talk from the Frontline
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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"Â by Rachel Schutt and Cathy O'Neil: O'Reilly, 2013 might just be the book that defines data science."Â -- Joseph RickertRevolutions Blog
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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.
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Product details
Paperback: 408 pages
Publisher: O'Reilly Media; 1 edition (November 3, 2013)
Language: English
ISBN-10: 1449358659
ISBN-13: 978-1449358655
Product Dimensions:
6 x 0.8 x 9 inches
Shipping Weight: 1.3 pounds (View shipping rates and policies)
Average Customer Review:
3.8 out of 5 stars
61 customer reviews
Amazon Best Sellers Rank:
#94,833 in Books (See Top 100 in Books)
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.
Book review - Doing Data Science by O'Neil and Schutt, O'Reilly Media.More breadth than depthWhat is data science? The book Doing Data Science not only explains what data science is but also provides a broad overview of methods and techniques that one must master in order to call one self a data scientist. The book is based on a course about data science given at Columbia University. However it is not to be considered as a text book about data science but more as a broad introduction to a number of topics in data science.In the spring of 2013 I followed two Coursera courses. One about the statistical programming language R and one on Data Analysis. I had for some time been looking for a book that could be used as a follow-up reading on topics in data science. This was the reason I picked up "Doing Data Science".The book begins with a chapter about what data science is all about is followed by four chapters on topics like statistical inference, explanatory data analysis, various machine learning algorithms, linear and logistic regression, and Naive Bayes. I have a background in both mathematics and statistics and I was able to understand these chapters but the material is covered in such broad terms that I find it hard to believe that a newcomer to this topics will understand or gain much knowledge from reading these chapters. Basic math is presented about the models but without some kind of detailed explanation one cannot develop any deeper intuition for the approach explained.The best parts of the book is definitely chapter 6 to 8 and 10. In here we find interesting discussion about coverage of data science applied to financial modeling, extracting information from data, and social networks. I really enjoyed the examination of time stamped data, the Kaggle Model, feature selection, and case-attribute data versus social network data. The math behind these topics was however once again explained quite superficial. Centrality measures is central to social network analysis but it is very hard to develop intuition for there measures without a more detailed explanation about the underlying math. These chapters contains lots of useful resources for finding additional information about the discussed topics.Data visualization is an integral part of data science for communication results. Beginners in the field of data science needs concrete and easy to follow instruction on how to get started with visualization. Unfortunately the book focuses more on the use of data visualization in modern art projects. The content is simply to abstract for beginners to learn about the usage of visualization in data science.When I was browsing the book before actual buying it I was kind thrilled to see that it covered topics like causality and epidemiology. Topics that I did not found covered in any other book about data science. However the chapter about epidemiology is not about using data science in epidemiology but 'just' about using data science to evaluate the methods used in epidemiology. Likewise there seems to be no link between data science and causality. I later discovered that the authors used an entire blog post ([...] to explain why causality was part of the university course underlying the book. This material or parts of it should have made it into the book. I am still not convinced that causality is a topic in data science.There are several examples in which the book assumes the reader to have knowledge of US government structure and organizations. Examples include page 292 when discussing US health care databases and page 298 where FDA is mentioned without further introduction or explanation about what FDA is.A book than contains programming examples should always make the code accessible to download. Typing in the code yourself is simply waste of time. It is possible to download some of the datasets used in the book through GitHub. But the code does not seem to be available. I also own the electronic version of the book and I tried to copy-paste some of the examples from the e-book but there are several examples of code that hasn't been proof written or tested prior to publication. The sample code misses references to required R libraries or refers to computer folder structures on some local Columbia University computer. The companion datasets that can be downloaded on GitHub consists of a number of Excel files. The R sample code uses the gdata package to load these Excel files into R for further analysis. It took be quite some time to figure out why this process didn't work on a Windows computer. The gdata package requires Perl to be installed on the computer and this is not default software on Windows. In my opinion one should always publish data in a simple format, e.g. csv files and definitely not proprietary formats like xls for Excel files.Data Science is both science and a lot of practical experience. I guess the title of the book Doing Data Science tries to capture that. You need to do data science in order to learn it. The covered topics are interesting but the material is more breadth than depth. Luckily there are lots of useful links and resources to additional materials. Personally I would prefer more details about the actual data science topics like e.g. extracting meaning from data and social network analysis and less focus on math. The book already requires some knowledge of math, statistics and programming, so why not presume that the reader has the background knowledge and dive straight into the data science discussions.I really like the idea about having a lot of different people present various topics in data science and the book is well written and contains lots of useful resources for further studies of data science. I will recommend to book to people new to the subject but be aware of the fact that source code is not available and that is a major drawback.Disclosure: I review for the O'Reilly Reader Review Program and I want to be transparent about my reviews so you should know that I received a free copy of this ebooks in exchange of my review.
We are in 2013, no one knows what the heck is “data science†but there are plenty of jobs out there. Here is a course for you, future data rockstar. Rachel and Cathy invited a bunch of people from industry to talk about a wide range of topics: from statistical inference to data visualization with plenty of algorithms, R code and data sets. This is therefore a hands on course with good theoretical depth. And the take away message is: if the world is a bunch of data pipes, don't just be a plumber. Rather behave like the freaking Mario Bros!
It's an OK book. It has some nice parts, although you don't learn data science from it. It's like a compendium of blog posts about where data science is used. There are some great articles on it, from interviewees, however, it lacks something.
The books is fairly dated, a lot of the exercises have broken links/outdated code.
Reading this book, you feel like you are talking to the authors. With 10 years of experiences in data analytics, I can still learn so much from the authors' way of explaining Big data to novice. I learnt from it and used it successfully in my work. As the expert within the organization, you need someone like the author who can "coach" you. Recommend to any Lead Data Scientists in Real World
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