Some years ago, during my studies at the University of Bonn, I had a Computational Statistics lecture. It was one of the elective courses within the Econometrics and Statistics specialization. Until today, you can read on the syllabus of this lecture:
“The course explains ideas and methodological issues of computationally intensive statistical methods. There will be a special emphasis on algorithmic and numerical aspects of practical implementation.”
I chose this subject because I wanted to specialize in Econometrics & Statistics and it seemed interesting. In these lectures, we gave a lot of importance to the practical part. We implemented many methods that Data Scientist use every day. We based our learning on a book that today is still one of the best positions for me to start your Data Science adventure. The title of this book is An Introduction to Statistical Learning. There is only one minus of this book, and that is all the practical exercises you can find in R programming language, but it’s only my opinion because I am not a fan of R language.
What you will find in this book?
In 2021, the second edition of this book came out. In the first one, among others, you could read about these topics:
- Sparse methods for classification and regression
- Decision trees
- Boosting
- Support vector machines
- Clustering
The second version expanded the book to include the following topics:
- Deep learning
- Survival analysis
- Multiple testing
- Naive Bayes and generalized linear models
- Bayesian additive regression trees
- Matrix completion
The book provides a simple way to understand the intuition behind the methods Data Scientists use every day. It doesn’t go into the mathematics of the methods mentioned, which is unnecessary at the initial stage. This book is a simpler, but more practical version of the more mathematically advanced book The Elements of Statistical Learning, which I also recommend for advanced readers. You can download the pdf version of this book completely free of charge from the authors’ website https://www.statlearning.com/
You will also find other reviews of the book on this page. One of them below:
As a former data scientist, there is no question I get asked more than, “What is the best way to learn statistics?” I always give the same answer: Read An Introduction to Statistical Learning. Then, if you finish that and want more, read The Elements of Statistical Learning. These two books, written by statistics professors at Stanford University, the University of Washington, and the University Southern California, are the most intuitive and relevant books I’ve found on how to do statistics with modern technology.
Dan Kopf, Reporter, Quartz
About the author
Arkadiusz Modzelewski
Owner & Blogger Data Science Hacker | Machine Learning Specialist | Data Science Enthusiast & Mentor