Resources to Supercharge your Data Science Learning in 2020

Advance your understanding of machine learning with this helpful collection of journals, videos, and lectures.

No matter your background or level of expertise, building up a store of knowledge across statistics, computer science, and machine learning is key to your success as a data scientist.

Web Resources

arXiv Sanity — the machine learning discipline is characterized by a particularly furious cascade of new information in the form of arXiv preprints. Fortunately, deep learning hero Andrej Karpathy developed this interface to make keeping up with the latest developments in the field just a little more attainable.

:bookmark: Foundational Papers — from the team at Aggregate Intellect (the group behind the ML Explained lectures discussed below), here’s a list of the most influential papers in the data science field.

:art: Distill — the team at Google’s Distill has produced a unique journal that offers remarkable visualization tools for groundbreaking ideas. For a more approachable entry point to the field of machine learning, check out Google’s AI Explorables .

:books: Paperspace AI wiki — this is the best AI glossary I’ve found so far… hmu in the comments if you encounter a better one!

:triangular_ruler: Short Science — focused on spreading ideas through the community, this online forum provides a platform for discussion of influential papers on machine learning.

:panda_face: Chris Albon’s blog — everything you ever wondered how to do in pandas, plus machine learning flash cards for purchase.

:cookie: Cookiecutter Data Science — look no further for a template to set up your next data science project.

YouTube Channels

:computer: Crash Course Artificial Intelligence — in this 20 episode series, Jabril lays out some fundamentals of machine learning. The Crash Course team also produced a great series on Computer Science that should be required viewing for all of us in the tech sector (and yeah, probably everyone who uses a computer).

:sparkles: ML Explained — Canada is like America’s clever older sister who charmed your high school teachers with her brilliance years before you got on the scene. Just when you thought Canada couldn’t seem any more perfect, she goes and makes ML Explained. This fantastic channel features lectures around an hour long that help provide clarity on seminal ideas in ML/AI.

Jay Alammar — as of this writing, Jay has produced one video, but I’m confident that this channel is one to keep your eye on for more great content to come. Jay is the incredible creator of Illustrated Transformer , A Visual Guide to Using BERT for the First Time , and more amazingly approachable content.

3 Blue, 1 Brown — this channel offers an excellent introduction to statistical concepts underlying the machine learning field. If Grant Sanderson can’t get you excited about learning math, not sure what will.

Podcasts

Nvidia’s AI Podcast — since the mid-2010s, Nvidia has been spearheading the rise of GPU computing for deep learning. This podcast features a charasmatic host and an impressive mix of enterprise leaders, startup hustlers, and tech updates.

:telescope: The Data Exchange — Ben Lorica tackles machine learning related business cases in each episode. The podcast’s website offers excellent documentation, plus always interesting “related content” with entries dating back to Lorica’s time at O’Reilly.

⚛︎ Journal Club by Data Skeptic — this podcast is like sitting in on your super smart friends’ study group. Kyle, Lan, George, and sometimes a guest meet to discuss the latest in data science journal articles and related news.

The TWIML AI podcast — beyond hosting an engaging interview series, TWIML has started offering podcast listening parties, which provides a unique, collaborative learning experience.

:chart_with_upwards_trend: Linear Digressions — Katie, an experienced data science educator, explains core ideas in statistics to Ben, an enterprise software engineer and pun enthusiast. This is a great place to start your data science learning journey. Also check out Katie’s Udacity course — this is how I started with DS while I was still picking up the basics of Python.

More Resources

What resources are you using to learn about data science? Drop a line in the comments.