Editor’s note: This piece was written in collaboration with SwitchUp, an online platform for researching and reviewing technology learning programs. Erica Freedman is a Content Specialist at SwitchUp.

rquery and rqdatatable are new R packages for data wrangling; either at scale (in databases, or big data systems such as Apache Spark), or in-memory. The packages speed up both execution (through optimizations) and development (though a good mental model and up-front error checking) for data wrangling tasks.

TLDR: Yes, and there are precise results, although they are not as well known as they perhaps should be.

I read BD’s (bandes dessinées or, as we say in English, graphic literature or picture storybooks) to keep up with my French. Regular books are too difficult for me. When it comes to BDs, some of the classic kids strips and albums are charming, but the ones for adults, which are more like Hollywood movies, are easier for me to read because I find the stories more compelling: I want to find out what happens next.

This post is by Phil Price, not Andrew.

Joël Gombin writes:

Data projects are generally organized in one of two ways: top-down (that is, starting with the business question) or bottom-up (starting with the data and working up to insights). But is there a “right way,” and is one approach better or more effective than the other?

This post is co-authored by Erika Menezes, Software Engineer at Microsoft, and Chaitanya Kanitkar, Software Engineer at Twitter. This project was completed as part of the coursework for Stanford’s CS231n in Spring 2018.

Import AI: #102: Testing AI robustness with IMAGENET-C, militarycivil AI development in China, and how teamwork lets AI beat humans