Developing Analytic Talent: Becoming a Data Scientist

I-Hsien Ting (National University of Kaohsiung)

Online Information Review

ISSN: 1468-4527

Article publication date: 13 April 2015

Issue publication date: 13 April 2015

592

Keywords

Citation

I-Hsien Ting (2015), "Developing Analytic Talent: Becoming a Data Scientist", Online Information Review, Vol. 39 No. 2, pp. 273-273. https://doi.org/10.1108/OIR-01-2015-0012

Publisher

:

Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited


Big Data, open data, social computing and cloud computing are probably the most fashion terms in IT in the last five years. One consequence of these developments is data scientists have an increasingly important role to play, especially with regard to Big Data analysis. In this book Vincent Granville defines terms, concepts and developments related to data science and data scientists very clearly, the intention being to show how to develop data analysis skills that are the cornerstone of data science.

There are eight chapters in this book, and they can be categorized into three parts. The first part addresses the fundamentals and concepts of data science and data scientists. In Chapter 1 the author introduces the concept of data science, especially to clarify real and fake data science, and 13 real-world data science applications are briefly discussed. Big Data is the main topic in Chapter 2, especially comparing differences between Big Data science and traditional data science. Then the author present some basic ideas about how to become a data scientist in Chapter 3, which helps motivate those new to the field to continue reading the remaining chapters.

In the second part of the book Granville introduces some of the technical content of data science, as well as application case studies. In Chapters 4 and 5 many data analytic tools and methodologies are introduced as essential skills in the formation of a data scientist. In Chapter 6 the author presents several data science application cases to give readers a more grounded understanding of how to apply the techniques addressed in Chapters 4 and 5.

The third part of the book, Chapters 7 and 8, are the conclusions, with Chapter 7 addressing how to begin a career in data science, and Chapter 8 offering useful resources for new data scientists.

As a data scientist myself, I am delighted to read this book, which has given me a better picture of data science and what it is that we data scientists do, especially with regard to Big Data. Therefore, 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.

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