The quality of your professional knowledge depends on the quality of the books you read. You are a job seeker or entrepreneur; knowledge of your profession should be from good books.
Data Science, the rising field for job seekers and businesses, has a great study scope. It requires studying machine learning, statistics, mathematics, data visualization, business, and programming like R or Python and databases. Big data technologies like Hadoop and Spark are in demand.
As the field is getting adopted by industries, the requirement for skilled professionals is also increasing. So for getting read with good knowledge should be fulfilled by good books. Here are the most important top ten data science books you should read in 2021.
Undoubtedly, it is the most important book to read in 2021. It covers the subject from a holistic and leadership perspective that makes it the top of the list. It has almost everything you must know about leadership or to be a good professional in data science. It not only covers the core data science and machine learning with illustrations in R and Python but also covers the business acumen required to be a leader.
It is good for a beginner, mid-level, and even executive-level reading. It can act as a manual for data science in an enterprise. It is the first of its kind which presents data science in the enterprise domain.
2. An Introduction to Statistical Learning: With Applications in R
It can be the first step if you want to master every field of data science. If you are fine with programming, especially in R, then it’s good to start with the book. It can give you the foundation of statistics and keep off with R programming. To buy this book click here .
It is a very nice book for the fundamentals of machine learning. If you have gone through the first recommended book, then it will be quite easy to understand. It explains machine learning more mathematically. It uses less technical jargon.
4. Python Data Science Handbook — By Jake VanderPlas
Python is the alternative to R programming. And this is a good book to start data science with Python. It is suitable for intermediate level data science aspirants.Again you can read this book in combination with the first and third books recommended in this list.
If you want are interested in learning concepts first and then implement them in any programming you will learn later, this is a starting point. It makes use of plain English to prevent beginners from being overwhelmed by technical jargon. It has clear and accessible explanations with visual examples for the various algorithms.
6. Mining of Massive Datasets — By Jure Leskovec, Anand Rajaraman, Jeff Ullman
As the name suggests, it focuses on thelarge datasets mining. You can learn to develop production-level models. The major topics covered in this book are mining data streams, MapReduce, building recommendation systems, link analysis, dimensionality reduction, and more.
This is another book for data science.It is written in very structured and systematic way. You can easily understand the entire big picture of how analytics is done, as each step is like one chapter in the book.
The book presents the fundamental concepts of data visualization .It is easy to understand how to make the most of the huge chunks of data available in the real world. It’s good to learn storytelling with data with this book.
It is a good book for beginners and advanced level data scientists. The keen focus is on business demands, which makes the book very practical and interesting. It also explains statistics thoroughly.
10. Deep Learning — By Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep learning is one of the trending topic today. So, it is important to learn it in detail. It is good to go with this book if you need an in-depth understanding of deep learning. The book explains in-depth how to approach deep learning problems without stressing on the coding.
As the responsibility of data scientists is increasing, above books can make you ready for the same.