Doing Data Science: Straight Talk from the Frontline Paperback – 3 Nov 2013
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"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."
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."
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 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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Top customer reviews
“Doing Data Science: Straight Talk from the Frontline” is a compendium of chapters that deal with data science as it is practiced in the real world. Each chapter is written by a different author, all of who have significant practical experience and are acknowledged authorities on data science. Most of the contributors work in industry, but data science is still so fresh and new that there is a lot of crossing over between academia and the corporate world.
A few of the chapters include exercises, but these tend to be too advanced and assume too much background material for an introductory book. The exercises still give you a good idea of what kinds of problems data scientists tend to grapple with. However, this book is definitely not a textbook and cannot be effectively used as such. The book doesn’t provide any background on R, statistics, data scrubbing, machine learning, and various other techniques used by data scientist. It is highly unlikely that any single textbook would be able to do justice to all of that material anyways, but a book of that sort could still have a lot of potential use.
There are two groups of people who would benefit from this book. The first are people who have absolutely no background in data science or any of its related fields, but would like to get a flavor of what data science is all about and are interested in exploring it for career purposes. The second group are people with significant technical background in one of the fields related to data science (programming, statistics, machine learning, etc.) who are interested in broadening their skills and would like to see how would their particular strengths fit within the broader data science field.
Not only is Doing Data Science informative it's also a light and engaging read which is no mean feat in a domain that tends towards the dry and dusty. The authors have certainly done an excellent job of bringing together a thorough grounding in the data science domain and pull together a lot of data from different areas into a book that is coherent and eminently readable. If I do have any grouch it's the title; I would have been inclined to call it Understanding Data Science rather than Doing Data Science which I think covers the content a little more accurately. Overall though I would thoroughly recommend Doing Data Science to anyone interested in understanding the field rather than simply implementing it.
If you are coming from different stream, I think you will struggle to appreciate the book!
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