- TCP/IP Protocol architectures
- how to deploy a server
- RESTful vs SOAP web services
- Linux command line tools
- the software development life cycle
- modular functions + the concept of writing tests
- why GPU cores are important
- client-side vs server-side scripting
..and that's just a subset. If you meet a data scientist who has familiarity with those concepts, it's because they either have a CS or IT background, or they taught themselves.
And be mindful that sometimes more detailed, patient, lower-level explanations are necessary - especially when writing docs.
R is fantastic at this: for example, @hadleywickham's httr vignette.
cran.r-project.org/web/packages/h…
Show examples of what that those options like, in code and output, and why a data scientist would be interested.
"A website or mobile app could access the output of your model based on any given user input." = that works.
"The model you trained on 1000 records could be trained on all billion records." = 👍👍
It's also very difficult for a DS to know what they *don't* know. They're likely used to being the computer-y one in their research group; so a self-assessment isn't a good gauge.
Ask them to explain things like localhost, HTTP responses, authentication..
Data Scientists are quick studies, I promise! But working with Meg to synthesize golden nanoparticles or w/e != deploying a model to production.