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“Data Science” has become one of the most trendy research fields in recent years, as well as a catchall rubric for various job descriptions and work functions. The cynics and skeptics, and there are many of those, contend that “Data Science” is nothing more than repackaged Statistics, with a bit of coding and hacking thrown in. Its proponents, however, point out that most practicing data scientists use a variety of skills and techniques in their daily work, and come from a vast spectrum of career paths and backgrounds. I tend to side with the latter group, but I too am an outsider to this field and am still trying to get a better understanding of what it really entails.

“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.
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on 4 December 2013
Doing Data Science is actually a bit of an oddity; an easy read in a deeply technical field. The book is based on a series of lectures and aims to inform the reader how data science works rather than simply providing a cookbook of recipes to carry out processes. For me this approach worked very well; after trawling through endless examples and documents of the data science flavour of the year map reduce I can finally say that I understand the process. Of course if you are labouring under a pointy haired boss who has heard the term data science in a meeting and is pressing you to use all its latest and greatest techniques this approach may seem a bit frivolous, but it's well worth the investment. Understanding the process will speed up your implementations and will also inform you as to what approaches are valid with what you want to do. I've played with the big data hero du jour map-reduce a few times and never really knew what I was doing; having read Doing Data Science I have a reasonable clue as to what is supposed to happen so I can at least sanity check my next attempt rather than just try and hack example scripts and read impenetrable howtos.

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.
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on 5 October 2014
A decent high level book covering data science in the world of 'big data' - gave me an initial grounding that I can build on through more detailed texts.
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on 30 July 2015
The book is good, but DON'T buy the kindle version, as the formulas don't show properly on kindle. It is a book about data science, and you probably want to read the formulas...
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on 19 September 2014
Top notch book, interesting and informative with excellent links throughout (I read the kindle version). Overall I would highly recommend this book.
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on 19 March 2015
Missionary and programmatic, but not too expensive to acquire.
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on 9 June 2014
I picked up the book with expection of learning what data science is in a non technical manner. However, this book is heavily geared towards statisticians! There are pages after pages for statistical formulas and computer codes (in R).

If you are coming from different stream, I think you will struggle to appreciate the book!
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on 27 May 2015
Very good.
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on 12 March 2015
No systematic approach at all. Too many words in bla-bla-bla style. Quite messy pile of conceptions and misconceptions. I bet the authors never had worked with real data up to analysis result outcome implemented on production llevel.
Actually this reflects a bit styke of those, who call themselves data scientists — it looks like they have decided to chose data science because they failed to be good software engeneers, good mathematicians, statisticians or whoever doing real work, but they finally found a niche where they can sale and trade.
Sorry I was quite direct, but this book is defenetly not the O'REILY best.
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on 28 March 2014
totally worth it , especially if you love wrecking your head repeatedly and want to do it for a living :)
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