Books to learn Data Science.
Tags: datascience, machinelearning, ai, productivity
Published: 2022-04-24 07:03:06

The best knowledge is still placed in the libraries; within
books. In this article, discover some of the top recommended
Data Science books catering to beginners.

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.


**1. Practical Statistics for Data Scientists**
              _By Peter Bruce and Andrew Bruce_
![DS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/g7ts0s3trrsagg38ezr7.jpg)

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.

**2. Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow**
_By Aurélien Géron_


![Data Science](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/kt3qzc2p28luf37lrm8k.jpg)

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression
and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.

**3. Introduction to Machine Learning with Python**
_By Andreas C. Müller and Sarah Guido_

![DS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qlj1ynbx5lpgalc27fzh.png)

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.

**4. Python Data Science Handbook**
_By Jake VanderPlas_


![DS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2650vl8te0yclf2ysa5y.jpg)

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 visualizations 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.

**5. Python for Data Analysis**
_By Wes McKinney_

![DS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/645qs80kw6xx8oqox8lu.jpg)

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.

**6. Python for crash course**
_By Eric Matthes_

![DS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ev9vlzn2wnfiyfzz9eoy.jpg)

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 visualizations, 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.