Top 3 Books a New Data Scientist Should Read

Photo by Paweł Czerwiński on Unsplash

Starting fresh in data science can be challenging sometimes. There are plenty of courses out there that promise to teach you everything from how to code to what is Principal Component Analysis (PCA) or train a Deep Neural Network. Some of them might do. But in my opinion, there are some books that can greatly benefit any data scientist. Shortlisting all the books I think are likely to be useful will rather confuse the reader as to where to start from. Besides I am sure there are tons of resources to find lots of books in this field. In this post, I will focus only on the top 3 books that in my opinion are sufficient to not just learn the basics but go far beyond. These books are rather classic (but still very relevant). If you are already a practicing data scientist who wants to learn about the latest developments in deep learning or so then this might not help you as much but still might provide you with some resources to refresh the fundamentals.

1. Machine Learning by Tom Mitchell

This is one of the first books I read to understand and learn about machine learning in general. In particular, I enjoyed the pages where it describes how a decision tree works; you know the building block of perhaps the more popular random forest approach. So, it teaches you to calculate entropy (the amount of information) recursively for each attribute/feature in your data. I encourage you to read that part as it is as straightforward as teaching it can get — check out page 52 onwards. The language is simple and there are plenty of details and examples taking you by the hand and showing you how actually the algorithm can learn patterns from the data.

2. Elements of Statistical Learning by Trevor Hastie

This book approaches machine learning from a statistical perspective, which I believe is essential to understand why machine learning algorithms actually work, what could maybe go wrong and understand the importance of having good data. The book spends a lot of pages on linear models but it is worth it as you will get slowly introduced to the concepts. But don’t think that this is just that. It covers everything including neural networks even if it doesn’t get into Deep Learning, which is a “recent” trend anyway! The examples presented along with the associated graphs help the reader grasp the concepts and gain an understanding of the described method. Just as a note, the book includes some maths and the corresponding math notation but don’t get put off by that. Just embrace how elegant formulas can capture the essence of the section. Besides, the description should be sufficient to understand the approach. Reading it is definitely worthwhile. It can also serve as a reference for data science practitioners to remind you of the model assumptions, differences between models and as a general data science refresher.

3. Pattern Recognition and Machine Learning by Christopher Bishop

The book needs no introduction. One of the best machine learning books ever, and recommended textbooks for the machine learning courses at Imperial College London, UK, a few years back. Starting with concepts that are crucial in machine learning like the curse of dimensionality, probabilities and distributions, decision theory and information theory to more advanced mathematical concepts required for machine learning. In order to follow the more advanced concepts, knowledge of mathematics and statistics would be useful. Nevertheless, a must-have book in your collection.


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