Dear friends,
Welcome to the 69th issue of the Sunday Briefing.
This week we are taking a break from blogging, but you can catch up on the most recent post in the Causality series were we cover the concept of d-separation, the fundamental tool in understanding how to make any pair of variables conditionally dependent or independent. As always, all the relevant code can be found in GitHub. You can also checkout the latest in the Epidemic Modeling series where we go over the basics of Network Structure, Superspreaders and Contact Tracing.
We’re proud to announce two new events coming up in November: Data Visualization with matplotlib __[Register]and seaborn and Deep Learning for Everyone __[Register]. Looking forward to seeing you there!
In our regularly scheduled content, we have a Guide to Training LSTMs, and overview of Data-oriented Programming in Python, a new programming methodology made possible by Python 3.7, an overview of Decision Table Patterns, a powerful but relatively unknown algoritm and a deep dive to Multi-Armed Bandits and the Stitch Fix Experimentation Platform.
On the academic front, we have an overview of Array programming with NumPy, A survey of Twitter research an interesting toy (and open source) election forecasting model and a look at the role of geography in the complex diffusion of innovations.
Finaly, in the video of the week, Daniel Chen teaches us about Cleaning and Tidying Data in Pandas
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjooy this newsletter, just go ahead and forward this email to them. This will help us spread the word!
Semper discentes,
The D4S Team
