Learning new terms and concepts in Data Science can be overwhelming. As Data Science has been one of the trendiest jobs that many are seeking, lots of resources are coming in handy.
Here are 6 such resources in the form of books that will help you on your journey towards being the next Data Scientist.
1. Machine Learning: A Probabilistic Approach
This is a very detailed book on machine learning topics ranging from the very basics of probability to mixture models, variational inference and deep learning. Rather than a book it is more of an encyclopaedia and can serve as a detailed reference for any data scientist or machine learning engineer.
The book doesn’t shy away from proper mathematical notation, which might be shocking for some, this is why the first couple of chapters about the basics are very important for you to get going.
There are diagrams exploring the characteristics of models, pseudocode, fully worked examples and even exercises at the end of chapters. There are a bunch of fantastic online learning resources for stats, ML and data science topics but most of them shy away from the maths and theoretical aspects which is where this book shines.
Author: Kevin Murphy
Education Level: Beginner-Advanced
2. Fundamentals of Deep Learning
Deep Learning is only getting more and more popular each year and with that, the wealth of online tutorials and courses about each topic keeps increasing.
The main issue with most of these is that they are either too focused on the implementation (which feels more like a tutorial for Keras than deep learning as a field) or they skip out on key theoretical concepts.
Unlike many other books, ‘The Fundamentals of Deep Learning’ uses easy to understand notation and minimal derivation while still covering the breadth of the field’s most common concepts.
The major advantage this book has, as an introductory text, is the inclusion of companion code samples in Tensorflow (the most popular DL framework) which makes the jump from reading and learning a topic in the book to actually implementing and experimenting seamless.
Author: Nikhil Buduma
Education Level: Beginner
3. R for Everyone
The solution to the often-thought problem that R requires too much knowledge for non-statisticians, R for Everyone draws on making learning easy and intuitive.
This book starts with the basics, walking you through downloading and installing R, but takes you through more advanced problems so you’ll be able to ‘tackle statistical problems you care about the most’.
You can expect to build both linear and non-linear models, use data mining techniques, use LaTeX, RMarkdown and Shiny, to make your code reproducible.
This guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.
Author: Jared P. Lander
Education Level: Beginner
4. The Elements of Statistical Learning: Data Mining, Inferencing, and Prediction
The Elements of Statistical Learning (popularly known as ‘ESL’) is often recommended as the next step in learning for machine learning (ISRL being the first step which is talked about in the next point).
The ESL text demands an advanced level facility with Algebra, Calculus and Statistics. Like ISLR, ESL does find mention as either an assigned or a recommended textbook in leading master’s programs in Data Science, Statistics and Business Analytics.
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Education Level: Advanced
5. An Introduction to Statistical Learning (with Application in R)
An Introduction to Statistical Learning (popularly known as ‘ISLR’) is easily one of the most popular textbooks available on machine learning. The text builds your machine learning concepts step-by-step.
Despite consciously restricting the discussions a little short of details on ‘mathematical derivations’ and ‘statistical jargon’, the text gives a complete treatment to respective topics.
Author: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Education Level: Beginner
6. The Cartoon Guide to Statistics
The Cartoon Guide to Statistics covers everything needed for a basic understanding of statistics. The authors use cartoons and humour to explain the concepts many find hard to learn. This book is great if you’re just starting to learn statistics and data science or if you want a good laugh while you refresh your memory.
The last page reads: ‘Well, that’s it! By now, you should be able to do anything with statistics, except lie, cheat, steal and gamble. We left those subjects to the bibliography.’
Author: Larry Gonick and Woollcott Smith
Education Level: Beginner
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