by Joseph Rickert
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. “Doing Data Science”, which is based on a course that Rachel taught at Columbia University and to which Cathy contributed, is ambitious and multidimensional. It presents data science in all of its messiness as an open-ended practice that is coalescing around an expanding class of problems; problems which are yielding to an interdisciplinary approach that includes ideas and techniques from statistics, computer science, machine learning, social science and other disciplines.
The book is neither a statistics nor a machine learning text, but there are plenty of examples of statistical models and machine learning algorithms. There is enough R code in the text to get a beginner started on real problems with tools that are immediately useful. There is Python code, a bash shell script, mention of JSON and a down to earth discussion of Hadoop and MapReduce that many should find valuable. My favorite code example is the bash script (p 105) that fetches an Enron spam file and performs some basic word count calculations. Its almost casual insertion into the text, without fanfare and little explanation, provides a low key example of the kinds of baseline IT/ programmer skills that a newly minted statistician must acquire in order to work effectively as a data scientist.
“Doing Data Science” is fairly well balanced in its fusion of the statistics and machine learning world views, but Rachel’s underlying bias as a PhD statistician comes through when it counts. The grounding in linear models and the inclusion of time series models establish the required inferential skills. The discussion of causality shows how statistical inference is essential to obtaining a deep understanding of how things really work, and the chapter on epidemiology provides a glimpse into just how deep and difficult are the problems that statisticians have been wrestling with for generations. (I found the inclusion of this chapter in a data science book to be a delightful surprise.)
It is not only the selection of material, however, that betrays the book's statistical bias. When the authors take on the big questions their language indicates a statistical mindset. For example, in the discussion following “In what sense does data science deserve the word “science” in its name?” (p114) the authors write: “Every design choice you make can be formulated as an hypothesis, against which you will use rigorous testing and experimentation to validate or refute”. This is the language of a Neyman/Pearson trained statistician trying to pin down the truth. It stands in stark contrast with the machine learning viewpoint espoused in a quote by Kaggle’s Jeremy Howard who, when asked “Can you see any downside to the data-driven, black-box approach that dominates on Kaggle?”, replies:
Some people take the view that you don’t end up with a richer understanding of the problem. But that’s just not true: The algorithms tell you what’s important and what’s not. You might ask why those things are important, but I think that’s less interesting. You end up with a predictive model that works. There is not too much to argue about there.
So, whether you are doing science or not might just be in your intentions and point of view. Schutt and O’Neil do a marvelous job of exploring the tension between the quest for understanding and and the blunt success of just getting something that works.
An unusual aspect of the book is its attempt to understand data science as a cultural phenomenon and to place the technology in a historical and social context. Most textbooks in mathematics, statistics and science make no mention of how things came to be. Their authors are just under too much pressure to get on with presenting the material to stop and and discuss “just what were those guys thinking?”. But Schutt and O’Neill take the time, and the book is richer for it. Mike Driscoll and Drew Conway, two practitioners who early on recognized that data science is something new, are quoted along with other contemporary data scientists who are shaping the discipline both through their work and how they talk about it.
A great strength of the book is its collection of the real-world, big-league examples contributed by the guest lecturers to Rachel’s course. Doug Perlson of Real Direct, Jake Hofman of Microsoft Research, Brian Dalessandro and Claudia Perlich both of Media6Degrees, Kyle Teague of GetGlue, William Cukierski of Kaggle, David Huffaker of Google, Matt Gattis of Hutch.com, Mark Hansen of Columbia University, Ian Wong of Square, John Kelley of Morningside Analytics and David Madigan, Chair of the Columbia’s Statistics Department, all bring thoughtful presentations of difficult problems with which they have struggled. The perspective and insight of these practicing data scientists and statisticians is invaluable. Claudia Perlich’s discussion of data leakage alone is probably worth the price of the book.
A minor fault of the book is the occasional lapse into the hip vulgar. Someone being “pissed off” and talking about a model “that would totally suck” are probably innocuous enough phrases, but describing a vector as “huge ass” doesn’t really contribute to clarity. In a book that stresses communication, language counts. Nevertheless, “Doing Data Science” is a really “good read”. The authors have done a remarkable job of integrating class notes, their respective blogs, and the presentations of the guest speakers into a single, engaging voice that mostly speaks clearly to the reader. I think this book will appeal to a wide audience. Beginners asking the question “How do I get into data science?” will find the book to be a guide that will take them a long way. Accomplished data scientists will find a perspective on their profession that they should appreciate as being both provocative and valuable. “Doing Data Science” argues eloquently for a technology that respects humanist ideals and ethical considerations. We should all be asking “What problems should I be working on?”, “Am I doing science or not?”, and “What are the social and ethical implications of my work?”. Finally, technical managers charged with assembling a data science team, and other interested outsiders, should find the book helpful in getting beyond the hype and and having a look at what it really takes to squeeze insight from data.