Every good blog should reference other good blogs. It's the way of nature. Following is a random-ordered list of blogs, videos and other resources I've consumed while evolving as a data scientist. Some of them are more basic (good for beginners), while others are more suitable for advanced readers.

- varianceexplained is a great blog by David Robinson, a data scientist at stackoverflow. It contains many great case studies.
- mathematicalmonk contains several lectures series.
I saw two of them: the
*probability primer*(which lays some foundations of probability; suitable for those who want to freshen up their probability knowledge), and the*machine learning*series which I can't recommend highly enough (it covers many algorithms with detailed mathematical explanations, but not too detailed to get you overwhelmed). - jbstatistics contains introductory statistics videos with basic examples of real data. Great for learning how to properly perform hypothesis tests and calculate confidence intervals.
- Christopher Olah has a nice neural networks blog. Specifically, I highly recommend the post about Neural Networks, Types, and Functional Programming.
- Probably Overthinking It is yet another great statistics blog. Take there is only one test for example.
- Think Stats is a nice online book about probability and statistics (from the author of the
*Probably Overthinking It*blog). It delivers all its ideas using python. It covers basic material, albeit too long and time consuming. I recommend it to those of you who want to get a better practical grasp of the material and have plenty of time (and for that I envy you). - unofficialgoogledatascience.com is written by google employees - enough said...
- Airbnb has a great data science team, and it shows.
- Evan Miller has great posts, e.g. bayesian average ratings.
- If you're into reading mathematical books, All of Statistics is a great one for building the formal theory of statistical concepts.

Is your favorite resource not listed here? Drop me a mail with a link!