Developing Analytic Talent: Becoming a Data Scientist

Developing Analytic Talent: Becoming a Data Scientist

Developing Analytic Talent: Becoming a Data Scientist

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.

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3 comments

  1. 111 of 116 people found the following review helpful
    3.0 out of 5 stars
    Messy stream of consciousness writing style handicaped the usefulness to its intended audiences, April 12, 2014
    By 
    J. Chang (PIttsburgh, PA) –
    (REAL NAME)
      

    Verified Purchase(What’s this?)
    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.

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  2. 51 of 51 people found the following review helpful
    1.0 out of 5 stars
    Self-promotional stream of consciousness, May 3, 2014
    By 
    D N (California) –

    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.

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  3. 49 of 50 people found the following review helpful
    1.0 out of 5 stars
    I suggest to avoid this book, April 25, 2014
    By 

    Verified Purchase(What’s this?)
    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.

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