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Hello! I know it’s been a while since I’ve released an article, but I’m excited to be back 🙂 . A question that I’ve frequently gotten by many of my followers is "what are the best data science books to read?"
While that is a subjective question, I’ll do my best to answer this in an objective and systematic way. When I assess the quality of a non-fiction book, there are four criteria that I look at:
- Depth: It’s important that the book of interest dives into the details and intricacies of the topic of discussion. If I can learn (almost) everything that I need to just from one book, I’d say it has high depth.
- Comprehensiveness: At the same time, the book also has to be concise and to the point. If a book provides unnecessary details or does a poor job at explaining complex concepts, it’s not a comprehensive book.
- Readability: Generally speaking, if a book is written well, it should be easy to read. This is somewhat related to comprehensiveness, but also considers the flow of the book – for example, a beginner book on machine learning should probably explain what decision trees are before it dives into random forests.
- Applicability: The last criterion that is arguably the most important to me is applicability. If the book can tie raw knowledge with how it can be applied in the real world, it makes the book exponentially more valuable.
With that said, here are the five best data science books I recommend you read in 2022!
1. Machine Learning Simplified
Depth: 4/5 Comprehensiveness: 4.5/5 Readability: 4.5/5 Applicability: 5/5
The first book on the list is called Machine Learning Simplified. It’s a new book that was published this year that covers the fundamentals of machine learning, and I must say I’m impressed at the uniqueness of this book compared to other ones! Whether you’re new to data science or need a refresher, this book stands out for several reasons:
- It comprehensively covers all of the core concepts related to data preparation and data modeling, yet deeply explains the mathematics behind the concepts. Therefore it doesn’t just answer the what but also how.
- It explains complex concepts and algorithms with applicable and intuitive examples, making ideas less conceptual and more practical.
- Most importantly, the book is supplemented with a GitHub repository that contains actual python code implementations of every topic that it discusses.
2. Practical Statistics for Data Scientists
Depth: 4.5/5 Comprehensiveness: 4/5 Readability: 3.5/5 Applicability: 5/5
Practical Statistics for Data Scientists is similar to the first book in the sense that it’s both comprehensive and detailed – the difference is that this book focuses on statistics instead of machine learning.
This book covers all of the core concepts that you need to know in statistics, including descriptive statistics, sampling distributions, hypothesis testing & A/B testing, and prediction.
As well, this book also includes code snippets in R and Python, ultimately allowing you to tie theoretical concepts with practical use.
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3. Naked Statistics – Stripping the Dread From the Data
Depth: 3/5 Comprehensiveness: 4/5 Readability: 5/5 Applicability: 4/5
Naked Statistics takes a casual approach at explaining core statistical concepts with real-life examples, answering questions like "How does Netflix know which movies you’ll like?" and "What is causing the rising incidence of autism?"
While I wouldn’t say it dives too deeply in statistical theory, it does a good job at explaining fundamental statistical concepts in a way that makes it intuitive and easy to remember.
Overall, I’d say this is a very entertaining and educational book at the same time!
4. Elements of Statistical Learning
Depth: 5/5 Comprehensiveness: 4/5 Readability: 3.5/5 Applicability: 3.5/5
The Elements of Statistical Learning is the most complete textbook out there on machine learning fundamentals covering everything from supervised learning methods, unsupervised learning methods, graphical models, high-dimensional problems, and more.
While it may not be as concise as the other books mentioned, you can bet that this book will thoroughly explain all concepts with mathematical explanations, visualizations, and proofs.
This book is also free, which is a bonus!
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5. Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
Depth: 3/5 Comprehensiveness: 4/5 Readability: 5/5 Applicability: 5/5
Business Data Science explains fundamental data science concepts in from a business perspective, making everything applicable in a business setting. I like this book because I feel like it addresses the main thing that many books fail to address, which how data science brings value to a business.
Through this book, you’ll learn the fundamentals of machine learning, how to apply these concepts in a business setting, and how to write R code to implement these solutions.
Thanks for reading!
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Not sure what to read next? I’ve picked another article for you:
and another one:
5 Things That I Actually Did at Work as a Data Scientist in 2021
Thanks for Reading!
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I hope you found this interesting and insightful. Let me know what your favorite data visualization from 2022 is in the comments!
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or you can check out my Medium page:
