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Présentation de l'éditeur

Learn what it takes to succeed in the the most in–demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one–of–a–kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you′re looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business–critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Quatrième de couverture

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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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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Amazon.com: 29 commentaires
84 internautes sur 84 ont trouvé ce commentaire utile 
Self-promotional stream of consciousness 3 mai 2014
Par D N - Publié sur Amazon.com
Format: Format Kindle
Data science is still a rapidly developing field, and one with an evolving definition. Because of this, a wide variety of specialties have stepped into the niche to plant their flag as the True Way to Data Science. Granville is just another example of this phenomenon.

While there is some good introductory information in this book (for lightly technical managers), it's incredibly light on both statistics and code, instead mostly offering narrative descriptions of motivations and algorithms. You won't find a lick of rigor in the 300+ pages. He also spends a lot of time trash-talking traditional techniques, rather than letting his direction speak for itself. Unfortunately, his narrative style can be described as rambling at best and incoherent at worst. Indeed, after putting down regression techniques as 'old technology' (does that make linear algebra even less valid?), he promotes that oh-so-fresh emerging discipline Six Sigma as one of the key components of data science.

I'm not kidding.

As other reviews have noted, Graville offered a 'bounty' for Amazon reviews, which is both against Amazon's rules as well as self-evidently unethical.
126 internautes sur 131 ont trouvé ce commentaire utile 
Messy stream of consciousness writing style handicaped the usefulness to its intended audiences 12 avril 2014
Par J. Chang - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
I had a hard time tracking the author's thought even though I am a PhD computational scientist with an MBA concentrated in quantitative analysis. I do not doubt the author's expertise but his writing style need some work to make this book truly useful to its intended audiences, thus stream of consciousness is not a good way to present analytical stuff.

According to the introduction (which is in the end of its kindle version, why?),

"The book consists of three overall topics: What data science and big data is, and is not, and how it's different from other disciplines (Chapters 1, 2, and 3) Career and training resources (Chapters 3 and 8) Technical material presented as tutorials (Chapters 4 and 5, but also the section on Clustering and Taxonomy Creation for Massive Data Sets in Chapter 2, and the section on New Variance for Hadoop and Big Data in Chapter 8), and in case studies (Chapters 6 and 7)"

Chapter 1 What is Data Science?
Chapter 2 Big Data is Different
Chapter 3 Becoming a Data Scientist
Chapter 4 Data Science Craftsmanship, Part I
Chapter 5 Data Science Craftsmanship, Part II
Chapter 6 Data Science Application Case Studies
Chapter 7 Launching Your New Data Science Career
Chapter 8 Data Science Resources
Introduction

The author spent three out of total eight chapters bad mouthing other disciplines and fake data scientists and educations and such. While I agree with many of his points, I do not think it needs three chapters to convey the messages. Moreover, the author should consider consolidate chapters 3, 7 and 8 into a single chapter concerning the data scientist career and training. I was really hoping to look for some wisdom in chapters about the craftsmanship of true data scientist. Well, I am sorry to say that I was rather disappointed because many of those topics were introduced rather superficially and there were really not much logical connections between the sections as the author's mind seemed to jump all over the places. Finally, the typesetting is also rather awful in its kindle version. I would appreciate greatly if it was done by LaTeX or Word.
65 internautes sur 66 ont trouvé ce commentaire utile 
I suggest to avoid this book 25 avril 2014
Par Stijn Vanderlooy - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
The book promises to teach you the skills needed to become a data scientist. However, it does not fulfill this promise at all.

The book reads as a collection of not well-thought fragments of the author's mind. Everything remains very superficial, connections between sections are often not logical, and examples are badly chosen. Sometimes a proposal is given to solve a particular problem but the solution remains high-level, has no theoretical foundation, and no experiments / comparison with existing techniques is done. Some sections are quite amusing to read (in a negative way when you are wondering why some sections have been included) but quickly this feeling fades away when you realize that you are wasting your time reading the book.

Honestly speaking, I cannot think of a target audience that could learn something from this book. Buy a good book on big data architecture or Hadoop and co if you are interested in that. You will find no information about that here. There are many machine learning / mathematics / statistics books with good reviews here on Amazon. The same with some recent books on data science that actually do give a good overview of the field. Please buy those to make sure you spend your time and money well.
115 internautes sur 124 ont trouvé ce commentaire utile 
This author practices 'Prohibited Seller Activities' by offering money for reviews 16 avril 2014
Par kaustubh - Publié sur Amazon.com
Format: Broché
The author's web page advertises "Write a book review, earn $250":
"Dr. Granville will offer 4 awards ($250 each) for selected book reviews published on the Amazon page where his new data science book is listed. Reviews must be published by June 30, 2014; we will select the four reviews that we like best."
[...]

The amazon guidelines explicity prohibit this practice:
"You may not write reviews for products that you have a financial interest in, including reviews for products that you or your competitors sell. Additionally, you may not provide compensation for a review other than a free copy of the product."
https://www.amazon.com/gp/help/customer/display.html?nodeId=200414320

The monetary compensation offer was distributed across statistics newsgroups by the author, which brought this unfair practice to our attention. It seems quite unethical and contradictory to amazon's stated guidelines.
41 internautes sur 42 ont trouvé ce commentaire utile 
Waste of time 26 avril 2014
Par I Teach Typing - Publié sur Amazon.com
Format: Broché Achat vérifié
I read the first 50 pages of and learned that hiring a data scientist is a good idea because they do neat stuff. There is nothing on how they do the tasks and no useful information whatsoever. Rather, this seems to be a marketing campaign full of quotable material suggesting that every company needs a data scientist. Perhaps there is useful information buried somewhere in here but this is the least useful thing I have bought in years.
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