Are you intrigued by the world of data science? Data science is in vogue now and is one of the hottest career paths for many students. However, tackling data science on your own can be quite daunting. It covers several topics like Statistics, probability, applied mathematics, machine learning, etc. However, there are many books that an aspiring data scientist can read. Reading these books can help an aspiring data scientist to not only get a basic overview of the subject but also to master it with enough practice. Here are seven books every aspiring data scientist should read:

**1. Think Stats: Probability and Statistics for Programmers by Allen B. Downey**

Aspiring data scientists need to learn about statistics. How can you integrate statistics with programming? This book gives you an overview of statistics, especially for data science. You will go through the core concepts of statistics and probability, which will help you in data analysis. The book uses data sets taken from the National Institute of Health. It has several examples of python code. The best thing about this book is that the language is very lucid, and it demonstrates real-world examples.

**Python data science handbook by Jake VanderPlas**

Since Python is such an essential language for data science, an aspiring data scientist needs to have a comprehensive knowledge of Python. This book teaches how to use Python for data science. It starts at the beginner level, but slowly, it progresses into more advanced levels. It covers a lot of topics like visualization methods, Numpy, data manipulation with Pandas, and also, Machine learning.

**R for Data Science by Garrett Grolemund and Hadley Wickham**

While you might keep your chief focus on Python, you should also have a working knowledge of R, another language used by data scientists. If Python does not have a specific library, R can provide you with it. This book can be used as a guide to help you perform data science projects on R. It covers several topics from R workflow, data visualization to data modelling.

**Machine Learning Yearning by Andrew Ng**

Machine learning is the future of the tech world. Machine learning has emerged quite recently in the data science field, but it has become quite popular in a short period of time. This book teaches data scientists how they could structure Machine Learning projects. It shows you how and when you should use Machine Learning and all the complexities that it brings.

**Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville**

If you want to enter the field of deep learning, then this can be your go-to book. This book teaches how applied mathematics can be used for Machine Learning and also emphasizes on Deep Learning. It shows the mathematics present behind certain deep learning concepts like regularisation, convolutional networks, recurrent and recursive nets, etc. While being mostly theoretical, it also sheds light on the practical implementations of these techniques.

**6. Storytelling With Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic**

Data visualization is a necessary part of data science. However, it is also a bit difficult. Data visualization creates narratives that can reach out to a wider audience. The use of unnecessary data can obstruct clear communication. This book teaches you how to get rid of the unnecessary data and create a proper narrative that touches the audience on a personal level. It shows you the art of storytelling using metrics.

**Ethics and Data Science by DJ Patil, Hilary Mason, and Mike Loukides**

As data scientists, you have to be aware of the ethical limits of collecting data and data analysis. In recent years, there have been several concerns put forward regarding machine bias, privacy, and data protection. This book helps you to understand the ethical principles in data science. It is a great book that gives suggestions to build ethics into the data science culture.

Read these books and set up your path as a data scientist. Happy reading!

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