- Paperback: 336 pages
- Publisher: John Wiley & Sons; 1 edition (9 May 2014)
- Language: English
- ISBN-10: 1118810082
- ISBN-13: 978-1118810088
- Product Dimensions: 18.3 x 2.5 x 22.9 cm
- Average Customer Review: 2.5 out of 5 stars See all reviews (24 customer reviews)
- Amazon Bestsellers Rank: 55,166 in Books (See Top 100 in Books)
- See Complete Table of Contents
Developing Analytic Talent: Becoming a Data Scientist Paperback – 9 May 2014
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"I strongly recommend this book for readers whose background is related to data science, statistics, information technology and management, computer science, business analytics, and so on." (Online Information Review, May 2015)
From the Back Cover
THE DEFINITIVE JOB SEARCH AND PREPARATION GUIDE FOR DATA SCIENTISTS
Data science is one of the hottest disciplines in IT, but much of the talk is just hype. The aspiring data scientist requires a resource that covers the important topics comprehensively and avoids the hype and buzzwords surrounding data science and big data. This book will show you exactly what data science is, how it differs from computer science, how to extract value from data and, most importantly, how to develop your data science skills to obtain employment.
- Source code, data sets, and a dictionary for review
- Sample resumes, salary surveys, and sample job ads for data scientists
- Detail into what companies are looking for in a data scientist
- Authoritative analysis of the big data and analytics industry
- Real–world job interview questions for a competitive advantage
- Cases studies for understanding analytics in practice
- Data science tricks, recipes, and rules of thumb
Top Customer Reviews
It took some doing but by page 4 I already knew that the author is more into self-promotion than actually telling you anything useful. Perhaps page 66 makes the authors approach clearest as he describes the weaknesses of various statistical methods and says that these weaknesses have been corrected in the last decade, while not giving a single reference to show why they were bad, or a single reference as to what has replaced them. Other than the unsupported comments that university lead research has not progressed, and that everyone is stuck using SAS because government says so, there is nothing much anyone in data analysis will not already know. There have been advances in data science in finance and equally importantly in the security services, but these are classified. Another lesson from those "advances" is that they are as fallible as the methods he criticises. They failed to predict 9/11 or the current financial crisis. But without a single reference there is no way to assess any of his claims.
Maybe it is a marmite book and some people will find it useful. For me any author who takes such a personal and unsupported position without balancing any of the arguments is a bad scientist. This is one to avoid.
The author goes on to 'demonstrate' how data science should be considered as a new profession, with its own sky-high salary range. The writing often shows an astonishing level of arrogance, either on the page or between the lines. Any opportunities to demonstrate the author's superior knowledge are seized upon. He states that the book will be most useful for students, executives and entrepreneurs, but terms are not explained, no attempt is made to introduce concepts and technologies which might be unfamiliar: we are expected to climb up Dr Granville's 'learning cliff'. As a result, the only people who might he able to get through this and find any benefit are those who are already working on analysis of Facebook likes or Tweets.
The author would have been better talking the reader through the process, from building a big dataset, modelling, through the processing and storage challenges, what regression and correlation analyses do and why they are used, statistical teams and how the stats work. Instead, we get bits of useless information like "i have never worked from a business case" and 'big data analysis is not to the faint of heart" and even, in the later chapters, the authors ideas for apparently unrelated projects like email encryption, improving Captcha systems, email marketing schemes (now we know who is to blame)etc etc. He's clearly and very intelligent guy but he is no teacher.
It's not a completely barren text.Read more ›
All of which is a shame, as the book does contain plenty of valuable and seasoned wisdom, but it's so painful to get at that I would recommend that only advanced students of data analysis tackle this book. In the introduction the author states that "much of the text was initially published over the last three years on the Data Science Central website". To be frank, it shows: as a series of articles intended for a core technical audience, this stuff is fine, but as an introductory or bridge guide, it is not.
Most Recent Customer Reviews
The core of this book is using techniques that require maths/statistics of university level, and there's a chunk about starting your career. Read morePublished 11 months ago by Emteq
Well, I never really wanted to become a Data Scientist, but what I did want to do was learn a little bit more about how data can help in the field of marketing. Read morePublished on 22 April 2015 by The Truth
Early in my career, I was a data analyst (not scientist). I know a reasonable amount about using data to get insights. Read morePublished on 2 Mar. 2015 by PeeBee
A compendium of new methods of data science, many developed by the author, along with a fairly robust criticism of existing techniques. Read morePublished on 18 Feb. 2015 by Feanor
Great Book! An ideal book for me anyway. I'm very interested in this area, but most treatments of it tend to veer towards the mathematical/algebraic side. Read morePublished on 8 Jan. 2015 by AlanMusicMan
Just so you know where I'm coming from with this review, I am writing as someone with a Computer Science degree, an MBA (University of Oxford), and I am a professional Business... Read morePublished on 3 Jan. 2015 by Ian Howlett
My review is short and to the point. Avoid this book. It is deriviative, superficial and overly pretentious.Published on 28 Nov. 2014 by Dr David Mankin
I was looking forward to reading this book but it turned out to be a bit disappointing. The book has 2 objectives: (1) overview of data analysis/analytics and (2) preparing for a... Read morePublished on 3 Nov. 2014 by Neil
I can only echo what others have said. My other half read this, he found it pretentious, conceited and totally self admiring. Read morePublished on 1 Nov. 2014 by Loopielou
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