It’s always energizing to chat with members of the TDS community, but it’s especially so in one specific way: realizing just how wide-ranging people’s learning habits and styles are. It makes sense, of course. People come to data science from diverse professional, academic, and cultural backgrounds, and what works for some might not do the job for others.

In this week’s Author Spotlight, for example, prolific contributor Khuyen Tran shared her tried-and-true method for digesting complex topics: teaching them. It’s only when Khuyen translates concepts into something others can understand and use themselves that they click for her, too. Read our Q&A with Khuyen, which covers a lot of ground—including the benefits of public writing for attracting potential employers and her thoughts on AI ethics.

Photo by Alexander Gamanyuk on Unsplash

For every learn-by-teaching data scientist there must be at least one (probably more?) who loves to dive into the deep end of theory before they go back into the world and find practical uses for it. If that sound like you, we have quite a few treats for you this week. Start with the latest research from Michael Bronstein and his co-authors, which introduces geometric deep learning as a framework with a double goal:

It serves two purposes: first, to provide a common mathematical framework to derive the most successful neural network architectures, and second, give a constructive procedure to build future architectures in a principled way.

Still in the mood for a lofty, speculative read? Gadi Singer wrote an engaging analysis of the promise of cognitive AI, and how “the integration of DL with symbolic reasoning and deep knowledge” will bring about this next phase in artificial intelligence. If even that isn’t enough, you can always turn to Robert Lange, who’s back with another round of his much-anticipated, monthly selection of recommended research papers.

If you’re more of a hybrid learner—someone who likes a gentle, comprehensive introduction to a topic, but enjoys an element of hands-on action, too—we hear you. There are many of you out there! Choose your adventure:

If you have a fresh take on studying data science or have accumulated some memorable experiences (good or bad) along your learning journey, consider sharing them with our community. You’d be surprised how many people might find your story helpful. (If you’re just taking your first steps, give our very own guide a try. It’s free and email-based.)

Thank you, as always, to all of you who read, contribute, and engage with the posts we publish—and special thanks go to all of you who’ve recently shared a TDS story with friends and colleagues. That means so much to us.

Until the next Variable,
TDS Editors

Recent additions to our curated topics:

Getting Started

Hands-On Tutorials

Deep Dives

Thoughts and Theory

What Kind of Data Science Learner Are You? was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.