Thank you for comment. Version control is crucial for software development. Data science has a lot of experimentation involved. In my opinion, not everything should be in version control system. My typical workflow is to experiment heavily with Python notebooks, without version control. If I have analysis procedure ready then I apply version control for Python notebooks or re-write code to Python scripts or modules. I think it all depends on what problem you are working on, and the experimentation level.

I'm the founder of MLJAR company, and we are working on AI assistant Python notebook. It is desktop app that aims to help starting in data science.