Developing Analytic Talent: Becoming a Data Scientist

Vincent Granville

Published by

Wiley

ISBN

9781118810088

RRP

£26.99

Reviewed by

A P Sutcliffe PG Dip CCI, MBCS

Score

6 out of 10

Over the past few years big data has become an increasingly important theme for those working in this field known as data scientists. This book highlights that there are many misconceptions about this topic, with confusion about what can be defined as big data, as well as just who might actually be called a data scientist and who is merely a statistician.

The author suggests that there are many people who assume that big data is only about performing statistical analysis on larger data sets, and he goes on to highlight that the role actually encompasses a much larger number of skills.

Amongst these is the ability to program, an appreciation of business processes, good communication skills, an intuitive understanding of the structure of the data being analysed as well as a solid grasp of statistical mathematics. The book provides a large amount of information about the methodology of the data analysis process, along with a number of examples to illustrate how the work might be undertaken.

It’s a seriously weighty volume, with a lot of technical material in the form of complex mathematics; it appears to be written as a text book suitable for advanced students who wish to move into this field of knowledge. Unfortunately, I found insufficient explanation to allow me to follow all of the material; and in addition, in many places, I was forced to re-read sections a number of times to try and follow the arguments proposed.

Although the book is structured in a way that might suggest it could be used as a reference work, I suspect that it might not be quite suitable for that purpose; I felt that most of the chapters would have to be read as a whole and perhaps even several chapters together in order to understand the points being made and then being able to make use of them.

The book clearly has a lot of important points to make; and for those that already work in this field, it would certainly provide some useful advice and guidance. Those with strong statistics or pure mathematics backgrounds might also find it of some value in guiding the way in which to proceed when starting to work in this area. However, I think it unlikely that it would to appeal to a larger set of people as it contains insufficient remedial information to help guide them through this complex topic.

Further information: Wiley

October 2014