I'm doing some research on data scientists and learning more about:
- what company size should you start having a data scientist on board? - as a data scientist, what does your workflow look like? - do you have any side projects? if no, why not? - how does your output look like for your data science work? (Excel, slides, API, database updates, etc.)
RStudio > write.table(x, “clipboard”, ...) > paste to excel > email data to BA who makes slide
Python/Anaconda + jupyterlab nbs + sklearn
Excel + Solver + PowerPoint
Obviously track everything on Git etc.
Many more things you can do here:
Use R notebooks, Jupyter notebooks, even have a build server and make each one of your projects an R package
Also, would you have a need for a build server?
I don’t have a need personally but some companies that are doing industrial scale modeling (on the order of building and maintaining thousands of models) do use a build server to Basically check that code is formatted properly and can have a model run in a somewhat automated fashion.
Thanks.
I'm curious about whether the rise of Python in data science is really just because of the lack of flexibility to integrate with other systems. I've read that R seems to do better in data science/analytics work but when it comes to integration, it's more challenging.
And any insights on which companies are already doing industrial scale modeling?