This article was originally published on Towards Data Science on June 8th, 2020.

With over 20,000 data science-related titles available only on Amazon[1], it can be a strenuous task for a newcomer to pick the best way to get into the industry. This article aims to solve this issue, providing you with 5 top-quality books on statistics, data science, but also a big picture of the field.

In the article subtitle, I’ve mentioned the idea of books being generally better than online courses for learning data science, or in fact, learning any other tech skill. If you are a huge fan of online courses I just hope you’re reading this paragraph, and haven’t already gone to the comment section.

Let me elaborate. **Books require concentration** to get through, especially technical ones. On the other side, with online courses I find myself watching the video through the end, but not really paying attention. Further, knowledge from the books will just “stick” better for the majority of us.

Don’t get me wrong, online courses are great for heavily practical stuff, but reading books is a way better solution for learning basics and theoretical concepts — something necessary before jumping to code.

Combining both — books for basics and theory, and online courses for the practical part will make sure you master both aspects in the long run.

Anyhow, you’re here for the top 5 books, so let’s see which the first one is.

### 1. Practical Statistics for Data Scientists

This book is a must for beginners as it covers a basic overview of all prerequisite concepts of data science. It doesn’t go too much in-depth, so it won’t bore you to death. You can expect to learn **basic concepts** of exploratory data analysis, what is random sampling, how to use regression to estimate outcomes, key classification techniques, and much more.

I find it great for beginners because it’s all about the basics which will be just enough to dive deeper into data science and machine learning. As the book doesn’t go into too much depth (perfectly fine for beginners), you’ll have to find additional resources once if you find yourself not understanding some more advanced concepts.

You can get it here.

### 2. Python for Data Analysis

Yet another great read and obvious next step if you’ve read the first book on this list. The book covers pretty much every possible method of **data analysis**, alongside with the basics of the **Python programming** language.

One thing I like particularly about this book is that the author gives you a good idea of what you should expect from working as a data analyst/scientist. To conclude, the book is very well organized and a pleasure to read, it’s perfectly paced and everything is explained simply.

You can get it here.

### 3. Inflection Point

If you’ve just read the first two books, you might be in need for a break from technical books. And that’s where this one comes into play.

If you’ve decided to get into IT, or data science to be more precise, it’s a good thing to take a break from all the technical stuff and focus on a **business perspective** for a bit.

This book wraps the author’s personal experiences and stories to get the reader familiar with *how’s* of the industry — how IT, big data, and cloud work. If you will seek a job in data science further down the road, it’s beneficial for you to know how it all ties together.

You can get it here.

### 4. Python Machine Learning by Example

You now know some basics of statistics, Python programming, data analysis, and a solid business grasp of your role, but also the roles of other people you’ll be working with. The next logical thing to learn is, you’ve guessed it, **machine learning**!

The book also covers the basics of Python. Since this part was already covered in book #2, you are free to skip it or go over it again to reinforce your knowledge.

Furthermore, the book covers some classic examples of machine learning in an interesting way. The author shares his experiences and best practices with regards to optimization and other things you might find useful on the job.

Overall, a great read.

You can get it here.

### 5. Hands-On Machine Learning with Scikit-Learn and TensorFlow

Certainly one of the longest data science books I’ve read but packed with amazing information. It is one of the easiest books to recommend.

Not only will you dive deep into machine learning, but you’ll also spend a significant amount of time doing deep learning in the most popular library — **TensorFlow**.

After reading and going through the exercises you’ll know just enough to dive deeper into deep learning, developing applications that solve real-world problems, but also you should know just enough to start applying for jobs.

You can get it here.

### Conclusion

If you are a beginner, reading though these 5 books (in the exact order) would take you a couple of months to complete. You could also take this a step further, and supplement practical part with **online courses**, just to get a different point of view.

If you cannot decide which additional video course to take, this article should help you:

Thanks for reading.

### References

[1] https://www.amazon.com/s?k=data+science&ref=nb_sb_noss_2

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