The field of experimenting with raw data and processing information, otherwise called data science, is for sure a very broad one. However, the field contains a lot of technical jargon. To make things easier today, luckily, a good number of data science books are available that you should start reading in 2021. Or, you could simply enroll in a data science course in Delhi to learn about the field.

A little trip to history reveals that 1962 was the year when data science and computer prediction models started to exist. Mathematician John W. Tukey predicted the effect of modern-day electronic computing on data analysis as an empirical science, and things have been growing exponentially ever since then.

With the evolution of technology, data and the methods of its usage have evolved as well. Let’s look at five data science books you should start reading in 2021.

  • Build a Career in Data Science

Author – Robinson Emily

Publisher – Manning Publications

Whether you’re a beginner or some experienced person, this book will help you climb ladders at any stage with its thorough explanation of technical concepts, algorithms, implementation, and understanding of job roles related to data science.

The unique selling point of this book is the incorporation of required soft skills and project planning explanation, along with all the technical stuff you will need to learn. Consider this book to begin with Data Science or aim at becoming a manager.

  • The Hundred-Page Machine Learning Book

Author – Andriy Burkov

Publisher – Andriy Burkov

Apart from all the other books that you read while studying through a data Science course in Delhi, this is our favorite pick. The content of the book is as interesting as its title. In this well-reputed book, Andriy Burkov aims to cover several complex AI systems and their use cases. The author aims to make many of the major concepts easy to comprehend and efficient to learn with the “as few words as possible” ideology in mind. The book is not exactly for absolute machine learning beginners, but for those who have had a little introduction and for old-time enthusiasts. Surprisingly, it also covers mathematics and its explanation in detail within a very few pages.

Major topics covered:

  • Structure of learning algorithms
  • Basic and fundamental algorithms
  • Neural networks
  • Supervised and unsupervised learning methods
  • Introduction to Machine Learning with Python

Author – Andreas C. Müller

Publisher – O’Reilly

If you’re just starting with machine learning and already have had your hands dipped in python, this is the best place to start with. This book makes learning fundamentals of machine learning easy even for a layman with its easy language and efficient examples. The focus is on understanding the practical aspects of using machine learning algorithms instead of draining out on the mathematics behind them. You’ll learn to create a successful ML algorithm with the following tools and libraries:

  • Python
  • Scikit-learn
  • Numpy
  • Matplotlib
  • Practical Statistics for Data Scientists

Author –  Andrew Bruce

Publisher – O’Reilly

The book was designed with the concept of specifying statistics for data science. A lot of mathematics is involved behind data science, which is rarely understood even by experienced data scientists because of lack of formal training. Practical Statistics for Data Scientists aims at providing a deeper statistical perspective. With the provision of almost all the concepts to master data science along with statistics and surprisingly reviews of the ML designs, this book is a must-have.

You will learn:

  • Applying various statistical methods to data models
  • Avoid misuse of these methods
  • Use of Random Sampling for a higher quality dataset
  • Use of regression to estimate outcomes and detect anomalies
  • Data Science and Big Data Analytics

Author –  EMC Education Services

Publisher – EMC Education Services

With beautiful graphics and illustrations to capture the reader’s mind, Data Science and Big Data  Analytics is a complete eye-feast to learn the analytics lifecycle. The pictures make sure you can see the real in-depth working of the entire system. With a very well-built structure and flow, this book introduces the relevance and thoroughly explains the operational concepts of Big Data with the introduction of secondary advanced analytics tools like MapReduce, Hadoop, and SQL.

By reading this book, you’ll be able to:

  • Contribute to the open-source network
  • Implement a lifecycle approach to data analytics problems
  • Use data to craft a story with data for business action


This sums up our top five recommended data science books you should start reading in 2021 to enhance your knowledge apart from the data science course in Delhi you’re pursuing. Data science and machine learning courses are a hot topic today. With the pandemic already taking a spike in certain parts of the world and recoveries on the other, utilizing the time to make the most out of the immensely populated data is a well-placed bet. Happy exploring!