Top Data Science & AI books for 2020

The content available online for Data Science and AI is increasing every minute. For all Data Science and AI enthusiasts focused on building their knowledge base in this ever-expansive field, we have the ultimate list of Data Science & AI books for the year of 2020. If you need a reading list for the year 2020, this is the right blog for you.

This list is an eclectic collection of all possible books based on the depth of the subject while keeping in mind the level of advancement of a newbie Data Science student. Let’s explore this list!

1. Data Jujitsu: The Art of Turning Data into Product  Author: DJ Patil
Original Publish Date: 17th July, 2012

Who else will be a better person to seek guidance from, other than the ex-Chief Data  White House, USA, DJ Patil? The co-author of the famous article Data Scientist: The Sexiest Job of the 21st Century, Patil, in his famous book “Data Jujitsu” portrays data science as the backbone to solve problems. This book has a detailed description of the problems that data-powered businesses and organizations face. There is a clear distinction between the problems that are impossible and the ones that are difficult.

A base outline hint to the solution of these problems is also given in this book. Intricate problems can be easily solved, if broken down into parts and applying data analysis for close examination. Data Jujitsu is an illustrative guide with different kinds of examples and suggestions to unleash the data power.

2. Python Data Science Handbook Authors: Jake Vander Plas
Original Publish: 2016 A

comprehensive guide, Python for Data Science Handbook has topics that progress from beginner to advanced level. This book perfectly introduces the important libraries, such as Scikit-Learn, NumPy, Matplotlib, Pandas, IPython etc., needed to work with data in Python.

Along with a practical aspect of how to implement different libraries, you will also find a number of effective chapters on data manipulation with Pandas, visualization methods, NumPy and machine learning etc. Go for this ready reckoner, whether you wish to brush up your basics or know the nuances of working with Python in data science.

3. R for Data Science Author: Hadley Wickham and Garrett Grolemund
Original Publish: December 2016

R for Data Science is a ready reckoner for data science professionals working in R. Amazingly visualized book with great clarity of the concepts, this book apprises you of Tidyverse and RStudio too. This book imparts knowledge on transforming, visualizing and modeling your data; various data structures; importing several kinds of data into R; workflow of R and data modeling. Grab this book to learn how to apply R to generate insights from the raw data. 

4. Think Bayes Author: Allen B. Downey
Original Publish: 2012

Do you know a programming knowledge? Use your skills to grasp other topics too, with the help of Think Bayes. This is an introductory book on Bayesian statistics, making the use of computational methods. Unlike other boodata.ks on this topic, this focuses on the practical part and uses Python coding and discrete approximations, in place of Mathematics and Continuous Mathematics. As a result, you will see that the integral in Maths is summation here and many of the probability distribution related operations become simple loops. This makes it an engaging book, instead of the usual boring book. Pick it up, even if you aren’t too friendly with the numbers because it is more of a practical representation, rather than a mathematical one.

5. Think Stats Author: Allen B. Downey
Original Publish: June 2011 

This book emphasizes simple techniques you can use to explore real data sets and answer interesting questions. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions.

6. Machine Learning Yearning

Author: Andrew NG

Original Publish: Draft Copy

Authored by one of the most coveted names of the industry, Andrew NG, Machine Learning Yearning aims at spreading the word about the right way to structure projects in machine learning. It is the right resource, if you wish to know how and when to implement machine learning and the right way to deal with the intricacies in the application of AI in real-life. Pick up the draft copy after you have had experience in AI; also, if you wish to be guided on some crucial aspects, such as learning curves, debugging inference algorithms, dev and test sets and others.

7. The Hundred-Page Machine Learning Book Author: Andriy Burkov
Original Publish: 2019

One of the crispiest introductions to the mammoth world of machine learning, The Hundred-Page Machine Learning Book is, by all means, a must-have book for all the professionals in the field of machine learning. This 100 page book is aptly suited for the busy professionals who find it tough to read a book due to their hectic schedules. But this is the beauty of the book that you won’t miss any basics and nitty gritty of the knowledge that a data scientist should have.

8. Deep Learning with Python 

Author: Francois Chollet
Original Publish: 2017

Deep Learning with Python is one of the most popular books in the field of deep learning. You will get to do programming whilst learning theory in this book. Keras library is the base of all the topics detailed in this book. This is because the author of this book happens to be the creator of Keras library. Who else would be a better choice to teach you the topics of deep learning? The book is abundant in practical examples and natural explanations.

Go for this book, if you wish to explore formidable topics and have an hands-on with the applications related to generative models, computer vision and NLP. You will gain practical experience o f applying deep learning in your projects.

9. Pattern Recognition and Machine Learning – Best for Theoretical Machine Learning

Author: Christopher M. Bishop
Original Publish: 2006

This book is a book for pattern recognition and presenst the Bayesian viewpoint forward. The book is well-structured with not much advanced level of mathematics used to explain the readers. Christopher Michael Bishop is known for his work Pattern Recognition and Machine Learning (PRML), has been awarded Tam Dalyell prize in 2009 and the Rooke Medal from the Royal Academy of Engineering in 2011. He is also a Laboratory Director at Microsoft Research Cambridge and a Professor of Computer Science at the University of Edinburgh

 10. Machine Learning for Beginners

Author: Leonard Deep
Original Publish: 2015

One of the best introductions to the field of statistical machine learning. introduces Machine Learning concepts in a very intuitive way with just enough maths that it doesn’t go over your head. This quick read introduces machine learning, and it could serve as a good reference book, or as a springboard for more in depth reading. That was our list for 2020 for all Data Science & AI enthusiasts..

Stay tuned for more updates on this blog. If there are any more books that you would like tosuggest, feel free to reach out to us in the comments section!

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