Kaggle Learn bills itself as "Faster Data Science Education," a free repository of micro-courses covering an array of "[p]ractical data skills you can apply immediately." As I'm sure you are well aware, there are all sorts of free and low-cost data science education alternatives available via numerous online platforms. So why am I feeling it necessary to write about another data science learning resource? As I plan to embark on a fresh fall learning initiative -- once Those Lazy-Hazy-Crazy Days of Summer are out of my system -- I wanted to first find some concise review material for concepts I have previously learned and skills I have already acquired but which may have gone a bit rusty on me. To be clear, Kaggle Learn does not bill its micro-courses specifically as review material; however, I am so far finding that they fit this requirement for me rather well (though, admittedly, I'm still early in the process).

Kaggle Kernels is our hosted data science environment. It allows our users to author, execute, and share code written in Python and R. Kaggle Kernels entered 2018 as a data science scratchpad. In 2018, we added key pieces of functionality that make it a powerful environment. This includes the ability to use a GPU backend and collaborate with other users. We had 346K users author kernels in 2018, up 3.1x from 111K in 2017.