10 Most Useful Books to Boost your Career in Data Science

With data ruling the world like never before, the opportunities it has opened in terms of career are immense. Having said that, the career scope in data science is great and so is the salary offered. This field is one of the highly reputed domains and will see even more growth in the years to come. But, what is worth mentioning is that staying updated with the technicalities is the key. Ultimately, you have to stand out from the rest and have an edge over others wherever possible. There are a plethora of options that you can choose from to get educated and develop all the skills required. However, books are one of the best ways to stay updated. But which books to prefer in order to make a move in the field of data science still remains a question. With that being said, here's a list of the most helpful books to keep your ball rolling.

Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller and Sarah Guido

Having knowledge about machine learning is critical when it comes to data science. And for beginners willing to get into data science can totally rely on this book. All the basics are very well covered in this book and have a lot of examples included making the learning experience even better. The programs are in Python but that shouldn't be a problem for beginners as Python too is explained in an understandable manner. This book focuses more on the practical aspects of using machine learning algorithms, rather than the math behind them.

Think python

Author: Allen B. Downey

This book is a great book to proceed with after having considerable knowledge about the basics of Python. Right from the basics of data structures and functions, to more advanced topics such as classes and inheritance, this book covers it all. With case studies that are included in this book, it is easier to gain practical knowledge.

R for Data Science

Authors: Garret Grolemund and Hadley Wickham

People involved in the domain of data science know-how important R programming is for this field. For the ones keen to know about R in detail, this book is the right pick. Concepts like data exploration, wrangling, programming, modelling, and communication are covered in the best possible manner. This book does require some prior knowledge of R.

Advanced R

Author: Hadley Wickham

Some firms would require only the basics of R in an employee. But having a deeper knowledge in R will only pave way for the best career opportunities with a handsome salary package. This book – Advanced R covers everything from the basics of data structures, object-oriented programming, and debugging, to the depth of functional programming and performance code.

Data Science from Scratch: First Principles of Python

Author: Joel Grus

This book, as the name suggests is for those programmers willing to get started in the field of data science. Though knowing Python is not a prerequisite to get started with the reading as there's a crash course on the same but if you already are a Python programmer then that'll just speed things up. This book majorly focuses on the implementation of machine learning algorithms.

Introduction to Statistical Learning

Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

Preparing for data science interviews cannot get a better start than going ahead with this book. This is one of the best books for all the beginners out there and also for the ones who want to brush up their knowledge. This book covers a range of topics from the basics of linear models to regressions, tree-based methods, and a lot more.

The Elements of Statistical Learning

Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman

After having gained knowledge about basics in statistics, it is now time to take a step further. With this book, updating statistical skills is easier than ever. Being a mathematics oriented book, it covers linear methods to neural nets, boosting, and random forests.

Introduction to Probability

Authors: By Joseph K. Blitzstein and Jessica Hwang

Needless to say, machine learning has a lot to do with probability and this book will help in having a better command on the various topics. Covering areas right from the core concepts to real-life problems, this book will surely build a strong foundation for data science.

Understanding Machine Learning: From Theory to Algorithms

Authors: Shai Shalev-Shwartz and Shai Ben-David

After having done with the basics, now is the time to delve deeper. This is the best book to proceed with in order to understand machine learning right from theory to the algorithms, exactly as claimed by the name of the book.

Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Deep learning is one of the most sought after fields in machine learning. Big market players like Google, Facebook, Amazon, etc. look for professionals skilled in deep learning. There cannot be a better way to gain expertise in this field than by reading this book. Algorithms – backpropagation, convnets, recurrent neural nets and attention mechanism are beautifully explained here.

Wait no more and update your skills with the best collection of books depending on your choice of interest! Happy learning!