Over the years, I have come across multiple resources I learned things from with ease. This is a page to track those resources. Please reach out to me if you think something belongs to this list. I will add it.
- Julia Evans has online zines for topics ranging from SQL, Shell scripts to Linux. These are hand-drawn explanations of stuff related to a particular topic. I stumbled upon here twitter post about how SQL actually executes a query and have been a fan ever since.
- All things ML, DS, CS, Stats. This is most comprehensive source I have ever seen.
- SQL Zoo. Hands down the best place to start learning SQL. This is where I learned from and advised many students to learn from as well.
- SQL Zines. Great resource for getting introduced to SQL. It has clear explanations with awesome visuals.
- Complete overview of SQL in one page.
- Chris Albon has a great repository of all things Python Data Science. It's just good to just scroll through and refresh your coding memory.
- Numpy visualized. His blog has other cool visualizations too.
- Dan Bader's website is a great place to learn python all around.
- Corey Schafer's YT channel. All python concepts are explain clearly.
- https://pyspark.itversity.com/03_data_processing_overview/01_data_processing_overview.html is the best I could find on pyspark
- DataCamp is a good place to get started. The career paths with 4-8 hour courses with bite sized videos and in website coding practice is really good and is the best way to get hands on with Data science.
- Mastering metrics (Introductory) and Mostly harmless econometrics (Intermediary) are great books to get a intuition of all this econometrics and causal inference. Search amazon.
- The effect is another great book. I really admire the author, Nick HK.
- Machine learning flash cards from Chris Albon is a great ML refresher. There are 300 hand-drawn cards explaining concepts related to ML. It costs $12 but is totally worth it. If that is a problem, he shares a card everyday on his twitter.
- Sebastian Raschka's book is great to get started on coding ML with Python. It's easy to follow and has great code snippets. His github has good lecture notes.
- Technical ML bible here
- 3blue1brown is the coolest educator I could think of. I learned so much from his videos. His series on linear algebra is my favorite.
- Eigen Values and Eigen Vectors. I don't think they update the website anymore. Cool visualizations nonetheless.
- Prof. Charles Geyer (UMN Statistics) has the best slides on Intermediate Theory of Statistics I, II.
- Aerin Kim's Medium blog - Very nice intuitive explanations of statistical concepts
- Introduction to Bayesian Data Analysis lecture series by Rasmus Baath - An amazing intuitive introduction to Bayesian thinking.
- Course catalogue for Causal Inference
- Rachel Thomas runs a cool blog on all things AI. I really loved her blogs on general advice in the field.
- Andrew Gelman's blog - If you have time to kill. Has some very nice answers to weird and unintuitive questions.
- This blog is now retired 😢
- https://ciechanow.ski/archives/ has great explanations with visualizations of how stuff works at a very fundamental level.
- https://www.typingclub.com/ is a great place to learn how to type. I typed by looking at keyboard for 12 years until I started learning to type. Its a great skill to have and I cannot think of better website to start.