Linear algebra concepts are underappreciated in data science (frankly, like many other math concepts). On the other hand, understanding some concepts, such as orthogonality, is critical to understanding methods like Double Machine Learning (and of course OLS and many other methods, but Double ML is the cool one).
There are several reasons for this lack of appreciation. The availability of ready-to-use, off-the-shelf libraries and packages is one reason. Another important reason is the lack of field-specific coverage of linear algebra with examples and applications for data science / modeling data.
I’ve discovered a new (free) book that addresses the second issue: “Linear Algebra for Data Science”. The book looks like a practical introduction to linear algebra, but at least each chapter ends with a subchapter called “Application to Data Science”.
In the words of the authors:
“We (Prof. Wanmo Kang and Prof. Kyunghyun Cho) have been discussing over the past few years how we should teach linear algebra to students in this new era of data science and artificial intelligence.”
Worth checking out: