Best Data Science Books for Beginners

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With the rise of podcasts and YouTubers taking over the social media world, informing people on what’s happened, what’s new, and more. The best knowledge is still placed in the libraries; within books. 

Learning on the web has become a new way of learning. However, most of these studies were all once upon a time written down. A lot of people are interested in getting into the world of Data Science, however, it can be difficult to choose which path to go down and the correct resources. 

There are hundreds of bootcamps, cheat sheets, and PDF reports you can choose from; however, how do you know which one is the right one for you without feeling overwhelmed?

I will go through some of the top recommended Data Science books catering to beginners.

Practical Statistics for Data Scientists

 
By Peter Bruce and Andrew Bruce

When you’re first thinking about getting into Data Science, a lot of people forget about the foundations of the sector; Statistics. Statistical methods are a key concept of Data Science, however, there are only a few Data Scientists that have a proficient understanding and knowledge of Statistics. 

There are courses online and books that you can purchase regarding statistics, however, there are not many available resources that cover statistics from a Data Science approach. 

If you wish to succeed as a Data Scientists, you will have to go through the different levels and understand each one at a good standard. This book allows you to go from understanding Data Science to mastering Data Science. 

In this book, you will learn about random sampling and how it can reduce bias and yield a higher quality dataset to using regression to estimate outcomes and detect anomalies.

Python Crash Course

 
by Eric Matthes

If you have chosen Python as your programming language to learn, this Python Crash Course book is the one for you. This book is the world's best-selling guide to learning the Python programming language.

You will learn the basics of programming such as classes, and loops, whilst learning how to write clean code, with exercises to guide and test your skills. 

Once you complete the introduction of the book and have a good grasp of understanding Python, you will move into implementing your skills with projects, data visualisations, and a simple deployed web application.

A lot of Data Science projects need the foundations of Python, so learning these are imperative and will help you improve your skills in Data Science, and be a foundation to you developing your career in the field. 

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

 
By Andreas C. Müller and Sarah Guido

Machine Learning is a very popular element in Data Science, with more and more people trying to transition from being a Data Scientist to a Machine Learning Engineer. 

This book is for Python users, however, if you have no prior knowledge of Python; this will help you learn the language whilst going through the book. 

This book will cover the basics of Machine Learning, giving you practical examples that you can go through and help you build a Machine Learning model by the end of it. It is for beginners that need guidance in understanding the basics of Python and Machine Learning. 

Once you have understood the concept, it is then recommended for you to move on to the Advanced books. 

Python Data Science Handbook

 
By Jake VanderPlas

Once you are feeling a bit more confident in your coding and understanding the concepts of Data Science, you will be ready to explore Python libraries. 

This book is an in-depth guide into Python libraries such as Pandas, Numpy, Matplotlib, Scikit-learn, and more. With these skills, you will be able to transform your data skills, analyse better and produce data visualisations to showcase your findings. 

This is a huge step in the world of Data Science and a lot of current Data Scientists' day-to-day works are surrounded by using these libraries. 

Python for Data Analysis 

 
By Wes McKinney

Although Machine Learning is booming right now, other aspects of Data Science are heavily used. Data Analytics is one of them. 

This book provides complete guidance with manipulating, processing, cleaning, and crunching datasets in Python. You’ll learn the latest versions of pandas, NumPy, IPython, etc, and be able to work with practical case studies.

Learning how to solve real-world data analysis problems is a great skill as a Data Scientist and is highly recommended. Most of your time as a Data Scientist is Data Wrangling, however, you can reduce the amount of time spent on it if you know the libraries and tools well. 

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.