There seems no end to the number of posts and videos, as well as books and other materials, that seek to explain data science at various levels and to various audiences. I try to read them as I come upon them in order, first, to discover what new things I can learn and, second, to determine if they might be useful as a basis for explaining things to others.
Data science is such a multitude of things: applied mathematics (statistics and probability, yes, but also linear algebra and calculus). I did not get a good foundation in any of these in my formal education, so I am having to make up for that. Mathematics and programming are best learned by practicing. I don’t get the chance to do that as much as I would like — it’s hard to find time to do the essential re-working of a professional career that tracked along one path for such a long time.
It’s hard to know where to start. The list below is a collection of things that I am currently working through in an effort to learn as I go. I keep it here on the blog to make it easier to access when I am traveling. (If I have access to a web browser, I am set to learn.) As of Spring 2022 this list is very much under construction: use at your own risk.
- Rinu Gour’s We are Living in “The Era of Python” is a brief intro to Python with a link to a Python course at its end.
- Data Flair has a list of lessons, including a Python Tutorial for Beginners.
- Dario Radečić’s The Ultimate Data Science Prerequisite Learning List.
- Dhruva Krishna’s Your historical, theoretical and slightly mathematical introduction to the world of Machine Learning stumbles a bit in its explanations, but his hand-drawn illustrations are quite compelling.