— In the News —
Numerous thinkers have explored the qualities that creativity must have, and most pick out two important factors: whatever the process of creativity produces, it must be novel and it must be influential.
So how does a machine determine what's creative? It's a network problem.
An AI system was recently credited with not just helping to solve a scientific puzzle but actually working out the details on its own. Here's a good overview of what it accomplished and how it works.
How machines think has a lot to teach us about how we think. Thought-provoking read.
There are lots of startups doing things that matter. Here's a rundown of many you may not have heard of yet and how data is propelling their success.
— Tools and Techniques —
The best thing about R is that it was written by statisticians. The worst thing about R is that it was written by statisticians...
This is worthwhile post that got a lot of attention around the web this week. Along with the 40 minute video, there's a good write-up that identifies R's most popular and important quirks.
These are key tips for working with databases. This article targets schema design but even if you're not designing databases, understanding how things work under the hood will be useful to anyone who works with SQL.
Great walk-through of a loan-level analysis of Fannie and Freddie data. The analysis is told as a story with code snippets, data visualizations, and discoveries along the way. The code, including scripts to access the data, is also available in a GitHub repo to make it easy to explore your own path through the data if you want. Highly recommended.
— Resources —
Python for Informatics is an informatics-oriented approach to learning Python. This is a free book and is available in a variety of downloadable formats. If you prefer print, it's also available on Amazon where it has very high reviews.
— Data Viz —
I don't typically link to course syllabi but this one is particularly worthwhile. This is for Jeffrey Heer's latest Data Visualization course at the University of Washington. The course is well organized and everything is linked, including required readings, course slides, and assignments. This is a fantastic collection of Data Visualization MUST READS and, with the assignments and slides too, it's a great resource for self-study. Highly Recommended.
Escher lets you build beautiful interactive Web UIs in Julia. If you're using Julia, you'll definitely want to check this out.