Chanin Nantasenamat Portfolio
Ex-bioinformatics professor turned developer advocate ("Data Professor") with 400+ tutorial videos and a GitHub of ready-to-run R/Python notebooks spanning cheminformatics and applied ML, backed by 160+ peer-reviewed papers.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Academic publication record backing applied tutorials
160+ peer-reviewed papers in computational drug discovery give his tutorials a depth check most content-creator data science material lacks.
Consistent, high-volume public teaching output
Producing consistent tutorial content over years proves an ability to explain technical work clearly and repeatedly.
Reproducible notebooks as the actual deliverable
Repos are structured as runnable companions to tutorials, so a viewer can execute the exact code shown rather than take a claim on faith.
What Data Scientists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If you left academia, treat your publication list as portfolio evidence — link it directly.
Make every explainer accompanied by runnable code in the same repo.
Consistency compounds: fewer, well-produced tutorials sustained over years beat a one-time dump.
Pick a domain (bioinformatics, finance, geospatial) to specialize your applied-ML teaching in.
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