Although digital media are making their presence felt with online courses curated with audio and visuals, making learning an interactive affair, the charm of books is not lost yet. Books are still the primary source of self-education and provide a holistic learning experience.
Data Science has been a popular career choice since Data Science courses have started creeping into the mainstream. Many have tried to figure out the nuances involved in Data Science all by themselves by using some incredible books covering almost the entirety of the subject matter. Some handy books are targeted at a beginner audience, and some are books that take you through specialization within Data Science.
In this article, we take a look at the top 8 books that are worth their salt and can equip you with all you need to take Data Science head-on.
Here is a list of the top 8 Data Science Books to learn Data Science in 2021
1. Statistical Learning with Sparsity: The Lasso and Generalizations
Authors: Trevor Hastie, Robert Tibshirani, Martin Wainwright
The book is targeted at learning the use of Statistics in Data Science. It covers significant parts of statistical learning. The authors take care to offer a concise introduction of the basics before elaborating on the subject.
The book discusses regularized statistical models and follows it up with examples and a bibliography section talking about details that include the historical development of the method.
2. The Field Guide to Data Science
Author(s): Booz Allen Hamilton
The book introduces the theme of Data Science by exposing tools necessary to work within Data Science. The book introduces the subject of Data Science well enough with great infographics and creative illustrations. The book also offers a guide on choosing the correct technique for each problem faced in Data Science.
3. An Introduction to Statistical Learning
Authors: Gareth M James, Daniela Witten, Trevor Hastie, Robert Tibshirani
This book is most often referred to or referenced in many machine learning courses. It does the job of describing machine learning techniques well while also explaining the basic statistics.
4. Convex Optimization
Author: Stephen Boy
This book, designed to cater to experienced folks in Data Science, attempts to introduce its readers to Convex Optimization theory, a concept used in Machine Learning/ Deep Learning algorithms to figure out the optimal parameters.
5. Head First Statistics: A Brain-Friendly Guide
Author: Dawn Griffiths
The book, like its predecessors in the Headfirst series, sets a friendly conversational tone. This turns out to be the best comprehensive book on data science. It covers the basics of statistics to lay a foundation based on descriptive statistics. Building on the foundation, the book takes you through Probability and inferential statistics with relative ease. The book covers all the critical foundational topics in a detailed manner. Supported by valuable pictures and graphics, the book constantly attempts to keep the reader interested. The book also follows up with easily relatable real-life examples helping you cement your learning. This book is highly recommended for beginners in data science.
6. Practical Statistics for Data Scientists
Another beginner-friendly book that gives an excellent overview of all the fundamental concepts required for learning data science. While it will skip the details, it does give you all the high-level ideas like sampling, types of distributions, randomizations, etc. Each of the concepts are well explained without dragging it too much. The book uses a generous number of examples to drive learning. The book stands out with its survey of Machine Learning models.
While covering all the topics relevant to data science, the book can still be considered a quick and easy reference. Since it leaves out the details, it cannot be used as a guide to mastering concepts or go in-depth into the subject of data science.
7. Introduction to Probability
Author: William Feller
One of the best books to learn about probability, this book hand holds a beginner and eases into the topic of Probability. The examples used are close to real-world problems, making them easy to understand and relatable. Although the book assumes no prior knowledge of mathematics and probability, it will be a great ready reckoner for those who have had a bit of a background in statistics in their foundational academic years. The book has been the choice of many for over five decades and should be a must-have on your bookshelf.
8. Python Machine Learning by example
Author: Yuxi (Hayden) Liu
The most accessible book to pick up machine learning skills, this book gets you started in machine learning in Python in a detailed way using interesting examples like spam mail detection. The author draws on his experiences in ad optimization, fraud detection, and conversion rate prediction, among others. The book assumes no prior knowledge of Python, but you will do well if you have a short primer in Python before you dig deep into this book. The book will help you set up the software for machine learning models. This book works well for both beginners and advanced users alike.
A good proportion of books listed above are available for free in pdf format. These books are a great way to cement your foundational skills in data science and machine learning. Taking up an online course in data science from a reputed institute offers interactive training at your own pace with a lot of hand-holding and live assignments. Books are a great way to acquire knowledge. In case you are looking to scale up quickly into data science and machine learning check out some of the well-curated data scientist courses and detailed courses on Data Science, Artificial Intelligence and Machine Learning.