Each month I plan to bring you a series of interesting resources in data science and data-science-adjacent fields. This month, in the inaugural post of the “Data Points” series, I’ll cover some publications that put out consistent, high-quality content on a regular basis. This should be enough to feed your data science appetite for quite some time.
I pride myself in the amount of data science and statistics concepts I’ve covered on this blog. One area I would like to explore further is machine learning, from theory to the cutting edge. One publication that’s already communicating big ideas in machine learning in an accessible and clear way is Distill. They already have a handful of articles, with more on the way. It’s a collaboration with Google Brain, OpenAI, YC Research, and other contributors (you’ll likely recognize several people on the steering committee if you follow the field). The first two sentences in the Google Research Blog article on Distill says it all: “Science isn’t just about discovering new results. It’s also about human understanding.” And that’s what Distill is about.
If you’re more interested in academia over industry and mathematics over engineering, you might prefer to follow Fermat’s Library. Every week, they release an annotated papers from various points through history (sometime very recent history), with notes that provide layman explanations, historical context, and suggested reading. They cover a range of subjects, including Biology, Computer Science, Physics, Mathematics, and Economics. There is enough content there already to satisfy the academia wonk in nearly everyone.
Data Science Weekly
Every Thursday, I look forward to an email newsletter from Data Science Weekly with current goings on in the data science world. They provide relevant articles and videos from the week, newly released tutorials, and even job postings for data science positions. If, like me, you follow many data scientists on Twitter or the information hub of your choice, then chances are you will have already seen some of the week’s picks. However, there are invariably links in the Data Science Weekly newsletter that slipped past me or did not make it into the general Data Science consciousness for one reason or another. This newsletter helps me make sure I’m up to date on all of the recent developments and conversations in the field.