The Data Science Roundup was started in September of 2015, coming up on three years ago(!). Over that period I’ve solidified what I like to cover, so I want to take just a second to share exactly what criteria I use to include or exclude a post. My goal is to make it clear exactly what you should expect in your inbox every Sunday morning.
I have a broad definition of “data science”. I care about, and cover, everything from data engineering to business intelligence to statistics to ML & AI to visualization. If you’re a data scientist, you should probably care about all of these things too.
I value applications over theory. Both are valuable, but I want this newsletter to first and foremost be useful. I favor content that showcases industry experts relaying their experiences solving real-world problems.
I only link to something once. Most of you are long-time, loyal readers. You don’t need yet another post explaining deep learning or Bayes’ Law—by now, I’ve covered those basics (feel free to revisit in the archives). Today, when I link to a post, it’s because that post brings something brand new.
Nothing is too advanced or too basic. Important ideas are sometimes complex and sometimes simple.
Every week I scan through headlines of 3-500 posts to get down to the 5-8 that I include here, and those are the rules. I hope you like the end product.
If you enjoy getting the Roundup every week, my only ask is that you spread the word by forwarding this email to three friends. The Roundup grows through your recommendations! As always, thank you: it is a privilege to have you as a reader. 🙏