Data Science has become a booming field of study, and many aspirants are looking out for opportunities to pursue a career in this exciting field. Learning data science through books will provide you with a more comprehensive understanding of data science since it incorporates mathematics, probability, statistics, programming, machine learning, and much more.

Here are some top books to read to comprehend data science principles better.

• Practical Statistics for Data Scientists - By Peter Bruce

If you're new to data science, this book will provide a thorough overview of all the concepts you'll need to understand. The book is not highly technical but covers fundamental concepts like randomization, sampling, distribution, sample bias, and so on. Each of these ideas is extensively covered, with examples and explanations of how they apply to data science. The book also includes an overview of ML models, which is a pleasant surprise. This book covers all of the topics required for data science. It is a quick and easy reference, but it is insufficient for in-depth mastery of the ideas because the explanations and examples are not extensive.

• Introduction to Machine Learning with Python - By  Andreas C. Mueller

This is a book that will get you started with Python ML. The concepts are presented in layman's terms, with several examples to facilitate comprehension. The tone is approachable and honest. Although machine learning is a difficult topic, after working through the book, you should be able to create your own ML models. You will obtain a thorough knowledge of machine learning techniques. The book includes Python examples, but no prior knowledge of mathematics or programming languages is required to read it.

This book is designed for beginners and covers essential topics thoroughly. However, reading this book will not be enough when it comes to ML and code.

• Data Science and big data analytics - By  EMC Education Services

This book discusses big data and its importance in today's technologically competitive world in a gentle manner. The complete data analytics lifecycle is discussed in depth, including case studies and appealing images, so you can understand how the system works in practice. The structure and flow of the book are outstanding and well-organized. You can rapidly comprehend the big picture of analytics since each stage is like a chapter in a book. The book covers clustering, regression, association rules, and many other topics, and easy, daily examples that anybody can connect to. The reader is also exposed to advanced analytics utilizing MapReduce, Hadoop, and SQL.

•  R for data science - By Hadley Wickham

Another book for beginners who want to learn data science with R. R with data science explains not only the concepts of statistics but also the type of data you would see in real life and how to transform it using concepts such as median, average, standard deviation, and so on, and how to plot, filter, and clean the data. The book will teach you about how untidy and actual raw data is and how it is handled. Data transformation is one of the most time-consuming tasks. This book will provide you with a wealth of information on various data transformation methods for processing so that meaningful insights can be extracted. 

•  Introduction to Probability - By William Feller

This may be the most outstanding book for learning about probability. The explanations are exciting and mirror real-world difficulties. If you studied probability in school, this book is a must-have to refresh your memory on the fundamental ideas. If you are learning probability for the first time, this book can help you create a firm foundation in the key principles, albeit you will have to study with the book for a bit longer.

If you're looking to launch a successful career as a data scientist, enroll in the IBM-endorsed Data Science Course in Delhi.  Master the in-demand skills, implement them in various projects based on your domain, and attend multiple interviews.