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Developing Analytic Talent: Becoming a Data Scientist Paperback – 9 May 2014

2.5 out of 5 stars 24 customer reviews

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Product details

  • 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)
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  • See Complete Table of Contents

Product description

Review

"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

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Customer Reviews

Top Customer Reviews

Format: Paperback Vine Customer Review of Free Product ( What's this? )
This book is aimed at helping you to get a job as a data scientist. The first part of that is trying to convince the reader that data science is something new and that lots of people have been getting on the big data band-wagon but the author is the only person in the world who knows what data science is. Everyone else is just in the game to rebrand their tired old courses and methods but Vincent is the man to see through their deceptions and to save the reader and turn them into the real thing.

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.
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Format: Kindle Edition
Although I'd read the mixed reviews beforehand, I purchased it anyway with high hopes - after all, there aren't many other books out there that claim to focus on the career of a data scientist. However, I have to concur with some of the other reviewers that it's messy and not particularly enjoyable to read. To me it reads more like a website or a blog, with some rather controversial opinions, lots and lots of lists, and very few citations to other work in the main body of the text apart from links to wikipedia(!). There is however a reading list in the final chapter, and some useful information sprinkled throughout, but arguably it's information that could already be obtained from the author's various websites. I'm going to order Max Shronn's book for a discussion of the softer skills of a data analyst/scientist which seems to be more highly regarded.
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Format: Paperback Vine Customer Review of Free Product ( What's this? )
early on in this book we learn that the only people who have the right to call themselves 'data scientists' are those who work with more than 100 million rows of data. This should have served as a warning of things to come.

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.
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Format: Paperback Vine Customer Review of Free Product ( What's this? )
The author of this book suffers from a common problem of academics: although he clearly knows his subject extrememly well, he is not particularly good at communicating that knowledge, and the result is a poorly written, difficult to read mess which is crying for the attention of a good editor. Additionally, most of the material presented here will require a good grounding in data analytics and statistics: although Mr Granville claims to offer an introduction into the topic for executives and Java programmers, even the introductory chapter is crammed full with bulleted lists of technical jargon which is only ever explained poorly, if at all. The premise of the book is that data science, and the analysis of "big data" is a rapidly evolving field, and many courses and guidebooks approach its problems using outdated tools and mindsets. Unfortunately in making this point, the author has a tendency to come across as rather holier-than-thou. At times his sniping becomes petty, for example when he berates a competitor's book for including an example showing the reader how to build a Twitter "what the author calls a word cloud", he feels it necessary to point out that this "has nothing to do with cloud computing", at which point I felt like shouting "YES, AND CLOUD COMPUTING HAS NOTHING TO DO WITH A CUMULONIMBUS, WHAT IS YOUR POINT EXACTLY?"

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.
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