5 Best Books For Data Science Newbies To Read In 2022

The field of data science is expanding at a rapid rate in terms of job opportunities, which has caused many people to wonder what exactly data science is and how one can start a career in this field. Thousands of individuals have used these books to learn data analysis, visualization, advanced programming, machine learning, and much more, even to get jobs!

So let's begin right away.

1. Python for Data Analysis-Learn to programme for data science.

If you have fundamental Python programming skills, this is a wonderful read and the logical next step. Along with the fundamentals of the Python programming language, the book covers almost every form of data analysis.

The author offers you a fair understanding of what you should anticipate from working as a data analyst or scientist, which is something we find to be very appealing about this book. As a whole, the book is really well put together, enjoyable to read, nicely paced, and everything is explained.

1. Fundamentals of Data Visualization-There are a thousand words in a picture.

What is the most efficient manner to convey the findings of your analysis? It's data visualization, that's right. This book explains how to solve the most typical data visualization issues and teaches you which form of visualization is most appropriate in each situation.

Despite having only 350 pages, the book covers all necessary material, including color scales, bar charts, distributions, QQ-plots, pie charts, mosaic plots, treemaps, scatter plots, time series, geospatial data, and much more. It also teaches you the fundamentals of successful chart design, a skill that data scientists absolutely must possess.

1. Storytelling With Data-Discover How to Deliver the Right Message

The basic idea behind this book is that you should use your data to tell a story rather than show it. Given that you are already familiar with the fundamentals of data visualization, it is a fantastic follow-up read to the book that was previously on our list.

You'll discover why context is a crucial factor to consider when selecting an efficient data visualization. Additionally, you'll learn how to design your thoughts and how to make your data visualizations clutter-free.

1. Data Science From Scratch-Outstanding Refresher and Much More

It is not sufficient to merely learn data science tools and libraries. It would be helpful if you understood the fundamental concepts, which is where this book comes in. Of course, if you've read the books on our prerequisites list, you already know the basics. An excellent summary and much more are provided in this book.

You can review Python programming, data visualization, linear algebra, statistics, and hypothesis testing in this 400-page book. It will also teach you the fundamentals of working with data and machine learning algorithms, from straightforward linear regression to deep learning, NLP, and recommender systems.

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1. A Must Read: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

This enormous book has over 800 pages! You're going to enjoy this, though. Because it thoroughly covers everything a person could need to know to work in the area, it has been a long-time best-seller on Amazon. Seriously, topics covered in the book range from the definition of machine learning through GANs and reinforcement learning.

The book progresses from elementary subjects like data collection, EDA, and feature scaling to true machine learning through algorithms like gradient boosting, decision trees, and random forests. Additionally, unsupervised learning and the primary dimensionality reduction approaches are covered. All that was contained in the first 300 pages!

The remaining material, from theory through application in the TensorFlow library, is reserved for neural networks and deep learning. ANNs, CNNs, RNNs, Autoencoders, and GANs will all be covered in great detail.

Summary

To sum up, data science is a wide field that requires a variety of talents from its practitioners. A good place to start is with these five books: Reading will prepare you to apply what you've learned to a subject that interests you.

If you want a thorough knowledge of the materials, plan on spending 6–12 months doing so. The amount of time you have available and your past expertise will determine how long it actually takes. For a smooth learning experience, you can even consider taking a data science course in Bangalore offered by Learnbay. Here, you’ll get live interactive classes, project sessions, hackathons, lifetime subscriptions to LMS along with job referrals.

Sairaj Tamse is an enthusiastic Data Scientist and passionate blogger who loves to write technical and educational content such as data science courses, Machine Learning and Artificial Intelligence.