Machine Learning and Data Science Resources You Should Know About by Elise | August 10, 2016





Mental Kaleidoscope



If you're reading this, you already know (or could reasonably conclude by powers of deduction) that we (Yhat) have a blog. The tagline of our blog is simple, machine learning, data science, engineering . Those are the things our team writes about a few times a week.

We like to think we have some pretty good ideas (flying a semi-autonomous drone around the office, for example), but those ideas are really just some combination of the team's thoughts, our reader's ideas, and what we read and steal from all of our favorite bookmarked sites/newsletters/blogs and community forums.

In the words of Austin Kleon (a very cool writer/artist), "Every new idea is just a mashup or a remix of one or more previous ideas."

Not surprisingly, someone clever said something similar over a century ago. "There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope..."

All that to say, here's some of the places we go to fill our mental kaleidoscopes.





Community Forums





Reddit

The first place I go to get a pulse for what's happening in ML and data science is Reddit. Some subreddits (how Reddit entries are organized) have a bad reputation for being troll-y or heartless, but IMO the community is honest and engaged at least for the pages that I frequent.

Yes, users get rather opinionated, but good posts make their way to the top via upvotes and the comment threads often have a lot of really good feedback.

Python Subreddit Great for staying up to date about new libraries/releases, tutorials and blogposts make their way here, plus lots of community support/active discussion here if you have a python question

Machine Learning Subreddit Smaller audience, slightly more academic feeling than the Python subreddit, lots of good arxiv papers, TensorFlow and Deep Learning are all the rage right now

R Subreddit For whatever reason, the R community on Reddit is smaller and less engaged than Python. TBH I don't check this one too often, though you can find the occasional gem.

Upside: Fastest moving channel I've found. Very specific thanks to good subreddits. Downside: If you post poo, you will be made aware of it, rather publicly.



DataTau

Obvious one to bookmark. Datatau is a Hacker News for data scientists started by a then grad student, Rohit Sivaprasad, in 2013.

In his words, "I want people — if they work on something cool in data science, I want them to post it there."

Upside: Short titles. Quick scan of what's happening in data science. Downside: It hurts my eyes. Not very discussion oriented.



HackerNews

Oldie, but goodie. Hacker News has been around since 2007. It's run by Paul Graham's startup incubator, Y Combinator (Yhat was YC Class of Winter 2015), and is a social news website focusing on computer science and entrepreurship. Well, sort of, anyway. "Anything that gratifies one's intellectual curiosity" can be submitted.

Upside: Broad and interesting. Downside: Easy to lose an afternoon to it. Less focused than the other sources I've mentioned.





Newsletters





DataScience Weekly

I love Thursdays. DataScience Weekly is a no-fluff newsletter with the editors' (Hannah & Sebastian-lovely folks) picks for data science articles & videos that appeared on the internet that week.

They also include a few job postings, training and resources, plus the occasional O'Reilly book.

Upside: Succinct and well curated. They've been at it for a while (Issue 142 tomorrow). Downside: You have to wait till Thursday to get it!



Python Weekly

Thursday is actually a double-whammy. Python Weekly also arrives that day. Pretty similar layout to DataScience Weekly, except it's also includes a nice roundup of interesting Python projects, tools and libraries that came out that week.

There's also a nice section for meetups/events/webinars for folks looking to get plugged in to the Python community.

Upside: Excellent descriptions of new Python tools/libraries. Downside: Not all article/tutorial links have descriptions--takes a little longer to figure out which ones are worth checking out.





Blogs



