Tutorial: Beyond the proof of concept: How to set up your data science project to scale

Nathaniel Jones

We have all seen talks about how (insert number above 95% percent here) many data science projects fail despite having a successful proof-of-concept. This talk is about design principles that you can apply to your POCs and data science projects to scale up and be successful. I will use python for the examples, but the principles apply regardless of programming language or your toolset. Only your mind, open source tools, and good teamwork are required. The talk will be aimed at everyone regardless of skill level and while some beginner level experience with coding will be helpful, it is not required to benefit from this workshop.