A Hands-On Introduction to Data Science

by Chirag Shah

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

400 pages, Hardcover

Published April 2, 2020

Reviews

[Basil Latif · Rating 4 out of 5]

This book is attempting to be a tour de force of the discipline of data science. As I am currently teaching a university data science course, I used the topic orderings in this book as the structure of my syllabus and and weekly lectures. This book does a good job of covering many fundamental, high-level data science concepts and the content is generally good. However, I found it lacking in several areas. The first 3 introductory chapters are good, but from there, the concepts get muddled and disorganized. In Part II "Tools for Data Science," the tools covered are UNIX, Python, R, and MySQL. UNIX is a nice-to-know tool as a data scientist, not a fundamental requirement. I skipped that chapter when preparing my course. Next, the book is using Python in some places and R in other places, which is inconsistent and confusing. I understand that there could be a benefit to teaching both tools in the same place and letting the student make a choice from there, so I won't criticize the book that much for this, but it is worth noting that depending on your language choice or preference, you will not find all examples in that language in this book. The book does a good job of interspersing real-life examples and having in-class activities which I do appreciate. The third section of the book covering Machine Learning is adequate but nothing special. I had to pull lots of my own examples from the Internet rather than using the examples used in this book. Overall, this book is a good attempt to covering the discipline of data science, but in my humble opinion, has some flaws.