Luke Barousse Portfolio
Data storyteller whose GitHub project analyzes ~790,000 real 2023 data job postings in SQL/Python to rank in-demand skills and pay by role — built entirely from a real, large, messy dataset rather than a toy CSV.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Large real-world dataset instead of a tutorial CSV
Analyzing ~790K scraped postings meant handling real messiness — inconsistent titles, missing fields, duplicates — exactly what take-home SQL exercises probe for.
The analysis answers a question with direct career value
Ranking which skills appear most in the highest-paying postings is immediately useful output, showing an ability to pick a business-relevant question and answer it end-to-end in SQL.
A widely-replicated teaching project
Hundreds of learners have forked this exact project as their own portfolio piece, indirectly validating the query design and insight framing.
What Data Analysts can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Source a large, real dataset rather than a pre-cleaned tutorial CSV.
Frame your SQL project around a specific, answerable business question.
Show your query progression, not just the final query.
Publish the raw dataset or scraping method alongside your analysis.
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