The rising demand for data scientists is an indication that you need to have sound knowledge about data.

When everyone is well aware of the fact that data forms the most crucial aspect for any business to achieve its objectives, who wouldn't want to be a part of such a promising industry? The very fact that organizations rely heavily on data to make informed decisions is good enough proof as to how important data is. This ultimately throws light on how much demand data science as a career enjoys. The rising demand for data scientists across the globe is an indication that you need to have sound knowledge about data to land a job in the magical world of data. On that note, here is a list of the top 10 must-read data science books for aspiring candidates.

Practical statistics for data scientists – By Peter Bruce and Andrew Bruce

This book is a novel delight for beginners as it covers a wide range of topics like randomisation, distribution, sampling etc. from the scratch. The language is absolutely easy to understand. Thus, someone with zero knowledge can make the best out of this book. 

Data science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data

This book by John Wiley & Sons covers a good number of activities and methods and tools that Data scientists use. Be it its concepts, principles, or practical applications, this book has got it all.  With the help of examples, you would be in a position to replicate using open-source software.

Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

If you are aspiring to enter the data-driven machine learning and deep learning avenues of the data science field, then this book is the one for you! Right from the fundamental practical aspects of data science to the applications of machine learning, this book will help you go through all of it.

Introduction to Machine Learning with Python: A Guide for Data scientists – By Andreas C. Müller and Sarah Guido

Machine learning is an important aspect of data science. Taking this into account, Andreas C. Müller and Sarah Guido have come up with this excellent guide. This book will help beginners understand the basics of ML and Python.

R for Data science

R is an important language for data scientists to have expertise in. This is where the book – "R for Data science" serves to be a blessing. This book covers a wide range of topics including data wrangling, programming, data exploration, data modelling, and communication, to name a few.

Python for Data Analysis – By Wes McKinney

Just like R, Python is also a popular programming language in data science. This is why "Python for Data Analysis" book is a complete guide for beginners looking forward to learning the concepts of Data Analytics with Python. This book will help you build real applications.

Data science from Scratch: First Principles with Python by Joel Grus

Through this book, Joel Grus emphasises the fact that aspiring data scientists should understand the ideas and principles before mastering the tools and modules. This is exactly what this book does – it shows how the tools and machine learning algorithms work by implementing the principles from scratch.

Python Data science Handbook – By Jake VanderPlas

For a beginner who is aware of the basics of Python, this book is just ideal. This book not just gives you an overview of Python but also teaches how one can work with Python libraries. Python libraries such as Pandas, Numpy, Matplotlib, Scikit-learn, etc., are adequately covered in this book. 

Data science for Dummies by Lillian Pierson

Yet another excellent book for someone who is aspiring to become a data scientist is – Data science for Dummies by Lillian Pierson. Topics like Data science basics, Big Data, Python, R, SQL, data visualization, real-time analytics, IoT, are all covered in this book.

Understanding Machine Learning: From Theory to Algorithms – By Shai Shalev-Shwartz and Shai Ben-David

As machine learning is an important concept under data science, an aspiring data scientist should have sound knowledge about the same. The book covers the basics of Machine Learning, algorithms in ML, additional learning models, and advanced theory as well.