Data science books for beginners

Best data science books

Introduction

The Corona Virus Pandemic has the entire world working in a new, unfamiliar territory. Bearing in mind the current situation, we have identified some data science books to help ensure that you never venture out into the realms of unfamiliar territory alone.

Besides, times of change often offer a rare opportunity to develop oneself further. The rise of AI and big data is fast precipitating a transition toward a growing demand for data science skills.

So whether you’re an aspiring data scientist seeking to take advantage of the dynamics of change or a full-fledged professional looking to broaden your horizon, you’ll find the books in this list to be useful in some way.

1. Data Science from Scratch by Joel Grus

This book introduces the fundamentals of data science as well as proven methods and techniques for applying data science tools and algorithms. It expresses concepts in a concise and easy-to-understand manner, thereby providing readers with added value in the form of specific information and insight into data science.

Data Science from Scratch introduces you to topics such as Python, Machine Learning, Linear Algebra, Statistics, Probability, and many others. It’s hard to look past this if you’re a newbie seeking an absolute beginner book.

2. Deep Learning with Python by François Chollet

Deep Learning with Python introduces the fundamentals of deep learning from an intermediate reader’s perspective. Francois Chollet attempts to make data scientists familiar with deep learning by employing all essential instruments of the Python Language and Keras library to provide them with key competencies.

In this book, you’ll learn how to set up your own deep learning environment successfully. The course also gives insight into text and image generation, neural style transfer, and image classification models.

3. Python for Data Analysis – Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Python for Data Analysis is an appropriate beginners’ guide to Python for data science. The book gives an introduction to data science tools in Python. It includes extensive practical case studies, thus making it a valuable guide both for experienced data analysts and readers who are new to the field.

Wes McKinney teaches through a step-by-step approach that places emphasis on practical experience and teaches basic and advanced approaches to manipulate data, Numerical Python (NumPy), and problem-solving.

Also, you will learn a lot about both the elementary and advanced aspects of data analysis tools in the panda’s library, familiarize yourself with time-series data, and create interactive visualizations using Matplotlib.

4. Introduction to Machine Learning with Python – A Guide for Data Scientists

Here, authors Andreas C. Müller and Sarah Guido attempt to show readers how to build an intelligent machine learning application through a combination of Python and the sci-kit-learn library learning techniques that together provide high-quality results.

You’re in luck if you already know how to use NumPy and matplotlib libraries effectively. Because this advanced course teaches you how to exploit them to their fullest. That is not to say that the book is advanced through and through.

Rather, it combines an expansive coverage of both advanced and introductory aspects of machine learning – even though discussions of the advanced aspects of the field are dominant herein.

5. Data Analytics for Beginners – A Beginner’s Guide to Learn and Master Data Analytics by Robert J Woz

This book deals with the basic aspects of data analysis and is specially designed to cater to beginner data scientists and intermediate learners who already have a fair knowledge of data analysis.

Using simple-to-understand language, the book introduces readers to statistics, data, and mathematical concepts that give a foundation for a data science career.

6. Naked Statistics – Stripping the Dread from the Data by Charles Wheelan

In Naked Statistics, Wheelan shows that statistics could also be taught in a fun and entertaining way. The book reminds, in practical steps, what statistics is about as well as how to apply it to machine learning.

Wheelan’s technical expertise and enthusiasm to explain complex concepts in a simple, comprehensible way helps to facilitate the assimilation of topics such as the Central Limit Theorem and regression analysis.

The necessity of statistics in data science makes Naked Statistics a must-have for beginners, as it treats both basic and more advanced topics on the subject in a clear, concise manner.

7. Make Your Own Neural Network by Tariq Rashid

Make Your Own Neural Network takes an entirely practical approach to helping readers understand neural networks and the idea behind them.

The book provides basic explanations of topics ranging from how to build a neural network using Python to understanding how a neural network functions, including how to run it on a Raspberry Pi.

Whether you’re a beginner with no background in computer science or a casual reader seeking to gain insight into neural networks, this book offers the right set of easy-to-apply concepts on an otherwise complex subject.

8. Automate the Boring Stuff with Python by Albert Sweigart

If like most computer users, you often get stuck doing one thing over and over when you still have to complete a variety of tasks, then this book is for you.

The text seeks to help readers understand how they can use Python to write programs that will handle tasks on their behalf without needing experience in programming.

Such programs can be set up to automatically search for individual documents or entire files across files, fill out online applications, edit PDFs, send notifications, and perform many other functions.

9. R for Data Science – Import, Tidy, Transform, Visualize, and Model Data

R for Data Science by Hadley Wickham and Garret Grolemund is a good way to turn it into meaningful information in order to serve your purposes. Readers need little to no data science experience in order to learn and apply the most effective data analysis strategies.

Here, you’ll learn Markdown as well as how you can use it to integrate results, how to examine data before you begin analyzing. Readers will also become familiar with how to generate hypotheses and how to test them thereafter.

Thus, R for Data Science is a unique opportunity to get a concise and practical introduction to all the essential topics of data science. It is also good preparation for a data science career for absolute beginners.

10. Behind Every Good Decision – How Anyone Can Use Business Analytics to Turn Data into Profitable Insight

This text by Priyanka Jain and Puneet Sharma seeks to empower people to make more insightful and timely decisions based on available information. Using Excel, Behind Every Good Decision teaches how to gather relevant data and convert it into insight so that better decisions can be taken at each point.

For this to happen, however, it is important that the business question is clear, and the plan necessary to achieve it, drawn. This concise book provides a solid foundation of knowledge for newbies and experienced analysts alike to truly understand data from the basics all the way to some of its most advanced core.

It treats the prominent techniques and methods of data analysis, advanced analytics team building, data analysis tools, as well as data-driven decision making, among others.

If you have any questions, please ask us here – we answer every single query of students. 

Final Thoughts

Whether you’re a beginner or a highly experienced data analyst, these highly engaging books offer more opportunities for practice and consequent growth. They bring powerful new insights that will help you understand data science more quickly while potentially saving you weeks and months of learning time.

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Image by Engin Akyurt