- Paperback: 336 pages
- Publisher: John Wiley & Sons Inc; 1 edition (1 December 2014)
- Language: English
- ISBN-10: 1118810082
- ISBN-13: 978-1118810088
- Product Dimensions: 18.5 x 2.3 x 22.9 cm
- Boxed-product Weight: 558 g
- Average Customer Review: Be the first to review this item
Developing Analytic Talent: Becoming a Data Scientist Paperback – 1 Dec 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
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Most helpful customer reviews on Amazon.com
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
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.
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.
[P. xxi] "This book is intended for data scientists and related professionals who are interested in shifting to big data science careers."
<Book reviewer:> If your current career involves analyzing some data, then you are just an ordinary data scientist. If you process over terabytes of data, then you can be called a real "big data scientist". I really have problems with author's way of treating data scientists.
[P. 5] "But logistic regression in the context of processing a mere 10000 rows of data is not big science; it is fake data science."
<Book reviewer:> We call you a BIG data scientist because you process over terabytes of data everyday. I guess only people working for Google, Oracle or Microsoft can be called BIG data scientist. ( I am just a data scientist. )
[P. 12] " Business analysts focus on database design"
<Book reviewer:> In my working life, I have never worked with business analysts who design database. Developers, DBA and system analysts usually can design database. Business analyst is a person who gather requirements from business and relate materials to I.T. professionals.
[P. 23] <Book reviewer:> Target stores are not selling well in February. It is because Target's competitors are all selling garden stuff. And Target did not know about that. How do you address this issue? [This is shocking !] Author does not give any real solutions. On the other hand, his solution is simply to hire some " visionary data scientists".
[P. 45] " Algorithm complexity ( the O notation) is more important than time spent in data transfers. ( WRONG: With Hadoop and distributed clustered architectures, optimizing data transfer and in-memory usage is often more important than algorithmic complexity."
<Book reviewer:> In the whole book, author empathizes the importance of fast processing [ speed, speed, speed ] and other technologies. When you are facing with so much raw data, technology is not that important, one must use his skills to solve the problem. The real tools are hidden between your ears, not in the servers.
etc etc etc........
This book is not written for beginners or students. It is written for "BIG data scientists" only.