Hacking the Data Science Challenge
summary
Claims a repeatable, time-boxed recipe for finishing a data‑science take‑home by automating the dull bits and delivering a story, not just plots. Shows a Colab notebook with pandas profiling and Altair examples, a simple modeling path (linear / tree baselines, avoid neural nets here), concrete pitfalls found in Citi Bike (negative durations, same bike in two places, a huge 1969 birth‑year spike), and produces reproducible predictions plus a results summary and next steps.
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chapters (11)
- 0:00Data science challenge intro
- 1:25Problems with challenges
- 3:10Trip data issues
- 19:21Data issues discussion
- 27:26Challenge outline
- 45:11EDA and modeling
- 59:50Pandas profiling
- 1:13:59EDA plots
- 1:19:47Results presentation
- 1:20:30Submission structure
- 1:24:41Portfolio tips
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