Aiming to dive into the ocean of Artificial Intelligence? Here are the most recommended books on Data Sciences to go for in 2021!

How many of you have come across several buzzwords like IoT (Internet of Things), Machine Learning, Data Science, Artificial Intelligence, Automation, and related cool technical stuff?  All of us have, at some point or the other. The way Alexa, Siri, or Google Assistant functions amazes us to the core and triggers our curiosity in ways unexplained, right?

Looking at the myriad ways in which voice assistants, home automation, social media platforms, and search engines have made our lives so much easier, there isn't any iota of doubt about the fact that the Data Sciences and Machine Learning sector will continue to expand and rise exponentially in the near future.

What do the statistics say?

The rise of a sector and the creation of job opportunities go hand in hand. Going by the job trend of 2018-2021 itself, almost all the global companies are constantly on the search for Data Scientists, Data Analysts, Machine Learning Engineers, Data Architects, and Statisticians to name a few.  In fact, it's the topmost preferred job amongst the trending job profiles in the States.

What's more? These high-demand positions come with lucrative salary packages and job security.

As backed by a study conducted by McKinsey Global Institute, Artificial Intelligence is estimated to create an additional 13-15 trillion US dollars of value annually by the next decade.

But, the element of surprise doesn't end here.  Well, it lies in the statement that AI or Data Science is not only for Engineers. Despite the fact that AI is now making huge measures of significant worth in the IT and software industry, a ton of the worth to be made later on lies outside this arena in areas like retail, travel, transportation, auto, materials, fabricating, etc.

So, what are you waiting for? Read on to know about the most recommended books on Data Science that are mandatory to be included in your reading list for 2021.

1. The Art of Data Science: A Guide for Anyone Who Works with Data

• Author: Roger D. Peng and Elizabeth Matsui   
• Publisher: Lulu.com 

If you're an aspiring data analyst and a beginner in the domain of data sciences, then this should be your inaugural book. "The Art of Data Science" is literally a conceptual analysis of the workflow of data algorithms.  

It's a no-brainer that only a keen data scientist can assemble and apply all the available tools to transform data into meaningful in-depth insights.  This book explains everything in a lucid manner and makes you comfortable with tools like linear regression, binary trees, generic trees, random forests to name a few. 

To pump you a bit more, the authors have written down the process of data analysis with minimal technical details to produce coherent results. Sounds interesting right!

2. A Common-Sense Guide to Data Structures and Algorithms

• Author: Jay Wengrow           
• Publisher: O’Reilly

Of what use is knowledge if it's not applied to solve practical problems! If you didn't get the hint, this famous book is a practical guide for all the data science enthusiasts and researchers out there who wish to leverage their skills.  

It presents crystal clear concepts and an astounding outline of the data analytics work process. Given the numerous interesting applications, and vast scope of data sciences, it could become a troublesome interaction for the amateurs to comprehend. 

However, the systematic approach taken by the author when writing this book will satiate your bubbling curiosity at each and every step, as you progress through it. The key topics included are linear regression, Big 'O' Concept, Trees, Recursion, and Graphs.

3. Essential Math for Data Science

• Author: Hadrien Jean           
• Publisher: O’Reilly 

Can you imagine trees without roots? No obviously. Similarly, science without maths is merely a concept with no practical existence. Keeping this fundamental aspect in mind, the author wrote the book, "Essential Maths for Data Science" to make students realize the beauty of data analytics and machine learning via mathematics.  

Even if you lack a strong mathematical base, this book will help you master it through topics like calculus, statistics, linear algebra, and probability.

You'll learn how to apply mathematical notions while formulating algorithms on Jupyter Notebook. What else?  you will be able to filter out data from huge data sets and plot graphs, perform matrices and vectors on frameworks like Keras and TensorFlow.

4. Data Smart: Using Data Science to Transform Information into Insight

• Author: John W. Foreman                                     
• Publisher: Wiley

Data Science, Artificial Intelligence, and Machine Learning are sectors that hold a great deal of relevance for everyone, ranging from engineers to business specialists. However, a majority of us are still confused about these terminologies, how it functions, and their workflow, right? Well, this book gives you a better insight into the outstanding world of Data analytics.  

Almost everyone nowadays knows how to work with spreadsheets, and hence John Foreman uses this insight to explain the filtering out of relevant data from bulk using spreadsheets. 

You'll get to learn about a host of topics ranging from data mining in graphs and k-means to supervised AI via logistic regression and mathematical notions.  It will also help you make the jump from using spreadsheets to R language!

5. Data Science from Scratch: First Principles with Python

• Author: Joel Grus                                                    
• Publisher: O’Reilly

As evident from the title of the book, "Data Science from Scratch" aims at building your concepts from the zeroth level of understanding. Hence, it's amongst the most famous Data Science books for working experts and understudies who aspire to be data scientists and AI experts in their vocations. 

The author uses python language to explain the demarcation between structured and unstructured data. It carefully guides you towards data filtering so that you can turn chunks of voluminous data into important insights for your firm.

With trending topics like the Internet of Things (IoT), Big Data, SQL, real-time analytics, and R language, this book will make you aware of all the tools, techniques, and modernization in the field of Machine Learning.

Isn't it a lot of useful and interesting information included in a single frame? If consistent and determined in one's approach, books will indeed reveal to you as true friends. Then what are you waiting for? Go and grab your favourite books and satisfy your curiosity about the universe of data sciences!

For more related information, read

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3. What does it take to be a great data scientist?
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5. NASSCOM rebuffs false claims of 3 million job cut In TCS, Infosys, Wipro, etc., says IT industry will continue to be a net creator of jobs