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Staying informed is so important. Guess which is one of the best sources to gain knowledge and stay updated? Books! These good old friends not only apprise you of the subject but also leave a lasting impression on your mind; you tend to remember everything that you read. And, when the field in question is the most popular- DATA SCIENCE, you ought to stay updated and regularly gain knowledge about the field with continuous development.

Data science is the process of deriving useful conclusions from the massive data, that may or may not be structured. Sounds not much of a job? Remember, truth is stranger than fiction. Howsoever easy it may sound, the complete process, right from data collection to reporting the findings, is not less than a tough nut to crack.

Get the pages flipping: Top 20 must have data science books

Whether it is to read about something new, seek answers to your queries, brush up your basics or stay updated, taking a plunge into the mammoth sea of books is the most wise step.

Before you actually start on your reading journey, see data science as one big umbrella that houses many branches/subjects. This can understandably be a little overwhelming when you are a fresher. Don’t worry! To make sure that you stay clear of all the confusion, here is a structured list of books, in no specific order, which spans across various subjects in the realm of data science.

Let’s start flipping the pages…..

The Structured List

  1. Data Science
  2. Python and R
  3. Probability and Statistics
  4. Data Visualization
  5. Machine Learning
  6. Deep Learning

Data Science

  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 Scientist at The 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. Data Science for Dummies

    Author: Lillian Pierson
    Original Publish Date: 20th February, 2015

    Data Science for Dummies aims at making data science simple for beginners. Count on this elementary guide to learn the business aspect of data science and start in the field as a data science professional. This book is a comprehensive resource for the starters that provides them with the basic concepts of data science and its applicability in daily life.
    Data Science for Dummies also houses a brief about topics, such as artificial intelligence, Python and R, data engineering, algorithms, data visualization, etc. Just curious about data science? Want to explain it to your grandparents or parents? Grab a copy now.
  3. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

    Authors: William Chen, Max Song, Carl Shan, and Henry Wang
    Original Publish: 2015

    Imagine, you are talking to the 25 of the top experts in the field and getting insights from them. Such a wonderful way! Right? The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists lets you do just that. It is a collection of interviews of top data scientists- from ex-Chief Data Scientist at The White House to the chief data scientists of the big organizations and the budding ones too. The aim is to let you have an exceptional insight into the industry, including tips on career advancement, try and fail and try again method and how to become successful in the data science field. Pick it up, if you wish to have an insightful and guiding companion and not if your wish is a technical expert.
  4. Ethics and Data Science

    Authors: DJ Patil, Hilary Mason, and Mike Loukides
    Original Publish Date: 25th July, 2018

    With the unprecedented rise in data science, there has been unwarranted inclusion of breach of privacy; rising need for data protection and other such issues. Ethics for Data Science has details on how to include principle of ethics into data science projects. You will find a practical checklist to refer to when building a project and some viable pieces of advice to practice ethics in the data science world. While in the world you will find a need to focus on the grievous issues, such as the misuse of data, this book will offer you some actionable points to deal with such problems and work on principles.

Python and R

  1. 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.
  2. Fluent Python: Clear, Concise, and Effective Programming

    Author: Luciano Ramalho
    Original Publish Date: 24th July, 2015

    You can always rely on the traditional programming book to learn Python, even when the market is flooded with several options that claim to be your effective Python trainer. Fluent Python: Clear, Concise, and Effective Programming is an ideal hands-on coding book that gives you an introduction about the working of Python and the key to writing useful Python code. This book includes many such libraries which you will definitely come across in your data science career.
  3. Natural Language Processing with Python

    Author: Steven Bird, Ewan Klein and Edward Loper
    Original Publish: June 2009

    When you are in the world of data science, natural language processing is something you will definitely be aware of. NLP will encompass computer manipulation of natural language. Taking the base of Natural Language Toolkit (NLTK) library, you will sail smoothly through the realm of Natural Language Processing (NLP) with the help of this book. Natural Language Processing for Python book has a practical introduction of the topic. Opt for this book to cover natural language processing in an ideal mix of theoretical and practical study.
  4. 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.
  5. R Cookbook

    Author: Paul Teetor
    Original Publish: January 2011

    A comprehensive and excellent book with more than 200 practical recipes to ease the road of data manipulation and data analysis. R Cookbook doesn’t have any recipes repeated; each one is unique and approaches a new problem. You will find a variety of solutions, such as basic ones, general statistics, graphics, input and output, linear regression and graphics. This book is a perfect fit for both beginners and experts. Pick it up as a ready reference for revising your concepts and learning fresh programming topics.

Probability and Statistics

  1. Probability: For the Enthusiastic Beginner

    Author: David Morin
    Original Publish: 2016

    Searching for a basic book on probability? Pick up Probability: For the Enthusiastic Beginner and start brushing up various concepts of probability. This is also an ideal choice for the ones who wish to learn probability from the scratch. Some of the topics covered in this book are- probability density, Bayes theorem, regression, combinatorics, expectation value. You will also come across few problems applying the concept of Calculus. Just like how a structured book should be, this book has around 150 solved problems, including the in-chapter questions and the ones solved at the end of each chapter. Choose this book, if you wish to have a guided resource on probability that has in-text discussion to make each topic comprehensible.
  2. 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 books 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.
  3. Think Stats

    Author: Allen B. Downey
    Original Publish: June 2011

    Starting with getting the hang of distributions and plotting, you will dive deep into two crucial topics- regression and hypothesis testing. Think Stats puts forth all the statistical concepts in a simple way, pairing it with real-world examples. If you have a basic understanding of Python then this book will be a guiding resource for you. This book serves as a practical analysis of statistics in the field of data science, including a variety of examples from Python coding and basic programs to detail on a topic. Choose this book, if you wish to learn computational statistical analysis, instead of the mathematical one.

Data Visualization

  1. Storytelling with Data: A Data Visualization Guide for Business Professionals

    Author: Cole Nussbaumer Knaflic
    Original Publish Date: 7th October, 2015

    One of the most important roles of a data scientist is to weave a story around the insights and thus, effectively communicate the findings to the non-technical business stakeholders. To make you adept in this task, Storytelling with Data: A Data Visualization Guide for Business Professionals lets you learn the data visualization fundamentals and storytelling skills. Empowered with real-world examples and illustrative text, you will be able to readily apply the learning to use data and present an intriguing, descriptive and fascinating story. Grab this book now to hone your storytelling skills and build on some new techniques too.
  2. Effective Data Visualization: The Right Chart for the Right Data

    Author: Stephanie D.H. Evergreen
    Original Publish Date: 22nd April, 2016

    Effective Data Visualization: The Right Chart for the Right Data is the creation of one of the most sought-after designers, researchers, and speakers, Stephanie D.H. Evergreen. This book details on the right way to create Excel graphs and charts that speak about your research and its insights. A comprehensive how-to guide, Effective Data Visualization: The Right Chart for the Right Data, includes a plethora of different graphs. It shows you which type of graph will be best suited for your specific story. The latest edition has details on nine new types of quantitative graphs, new shortcuts in Excel and a new chapter “Sharing Your Data with the World” that explains how to use dashboards, along with latest illustrations.

Machine Learning

  1. 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.
  2. 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.
  3. Elements of Statistical Learning

    Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Original Publish: 2001

    A sequel of ‘Introduction to Statistical Learning’, The Elements of Statistical Learning, approaches the machine learning algorithms in an in-depth manner. You might be apprehensive about the way content is covered in this book; don’t worry, the stress is on the concepts, which are explained with a statistical approach, not on mathematics. This is an illustrated guide with ample of examples to explain every concept and covers a wider range of topics from supervised learning to unsupervised learning. You will find a variety of topics, such as neural networks, ensemble methods, path algorithms, random forests, support vector machine and others. Pick it up, if you are a statistician or a data science professional and wish to dive deep into the world of machine learning.

Deep Learning

  1. 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 of applying deep learning in your projects.
  2. Applied Deep Learning with Python

    Authors: Alex Galea, Luis Capelo
    Original Publish Date: 31st August, 2018

    Filled with illustrated examples and natural explanations, Applied Deep Learning with Python is an ideal resource for building Python skills of the ones with no data science background. This book prepares you for the real-world skills needed in deep learning, through continuous activities and exercises. You will get to learn about basic skills like key visualization libraries, Jupyter environment, data sanitization techniques and others; right before your first predictive model is ready. Grab this book and hit the world of data science with out of the world Python programming.
  3. Neural Networks and Deep Learning

    Author: Michael Nielsen
    Original Publish Date: Free online book

    Neural Networks and Deep Learning lets you dive deep into the world of the neural networks. This free online book teaches you deep learning as a cluster of robust techniques to work with neural networks. Gradually moving from basic topics to advanced ones, this book also puts forth the modern techniques in machine learning. Pick it up to have with you written codes that make use of deep learning and neural networks to intricate problems related to pattern recognition (face, voice and image).

This ever evolving and constantly changing field of data science calls for being an active professional- in terms of learning, staying updated and constantly reading to gain knowledge. Who knows when you will be required to make use of the concepts you learnt in your graduation or apply some of the most advanced techniques? Go through the above list and start reading now!


Senior Content Writer @INSAID. Data Science and AI enthusiast who loves to read, write and converse.

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