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).
  • 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!