When a popular data science advice falls on deaf ears
Pitfalls to avoid for new data scientists
Build a project portfolio.
Arguably the most pervasive advice in data science.
Listening to the excellent Build a Career in Data Science Podcast, I was surprised to learn few people heed this advice.
A portfolio showcases your interests, skills and abilities to reason about data. It can convince a hiring manager to give you a chance, and it’s also effective for learning and joining a community.
So why do few take this advice?
Some theories:
1. Peak performance happens when you’re stretched beyond your comfort zone, but not too much into the panic zone (see Yerkes-Dodson). Failing to distinguish the two zones can lead to frustration.
The hosts advise not feeling bad about pausing a project, but to keep looking until a better fit is found.
2. The last mile is hard. Sometimes this involves polishing and deploying an app. Tools like Shiny in R or Streamlit in Python can help. Communicating with a clear GitHub Readme and a well written blog post goes a long way.
3. Scoping a project yourself is qualitatively different from following a tutorial. Owning the problem is not easy.
This is a journey everyone has to take.
I will share everything possible to make scoping and finishing your own projects easier.
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