Using Streamlit for proof-of-skill data projects
Proving to others that you are a skilled data scientist is notoriously difficult. Anyone can put Python or machine learning on their résumé or even work in a research group at a university that might involve some machine learning. But often, recruiters, professors you want to work with, and data science managers rely on things on your résumé that are proxies for competence, such as having attended the “right” university or already having a fancy data science internship or job.
Prior to Streamlit, there were not many effective ways to show off your work quickly and easily. If you put a Python file or Jupyter notebook on your GitHub profile, the time it would take for someone to understand whether the work was impressive or not was too much of a risk to take. If the recruiter has to click on the right repository in your GitHub profile and then click through numerous files until they find a Jupyter...
