Here are three links that are worth your time.
Lastly, here’s a map of the
most well-funded AI startups in each of the the US states.
Here are three links from the data science world that are worth your time.
Actionable data science in sales. Let your customers talk for 4+ minutes. Don’t talk about your company for more than 2 minutes. And more data-driven advice.
How to get into natural language processing. YCombinator has started a series of blog posts titled Paths on getting started with emerging fields. The first is on NLP.
The current state of automated machine learning. An overview of libraries that automatically apply machine learning techniques to datasets.
But remember: amidst all this big data, we have a bigger small-data problem.
Here are three links you should go through this week.
What I learned recreating one chart using 24 tools is an excellent comparison by Lisa of 12 visualisation applications and 12 libraries, with a good summary of which tool to use when.
Can we predict flu deaths with ML and R? Read this R notebook for a step-by-step walk-through of predicting whether a patient will survive or not. (There’s also a part 2 that improves on this model.) One of our colleagues nearly lost a piece of analysis recently. Here’s the
most boring / valuable advice she can get on how to organise analysis — or any form of work for that matter. Of course, you could always learn git.
If that doesn’t fix it, git.txt contains the phone number of a friend of mine who understands git. Just wait through a few minutes of ‘It’s really pretty simple, just think of branches as…’ and eventually you’ll learn the commands that will fix everything.