Talk Data to Me - the art of storytelling
summary
Shows a concrete pipeline: clean missing values, derive age from birthdate, and create features with Pandas, Matplotlib, Seaborn to inspect distributions, boxplots and a correlation heatmap. Uses the Harambee youth employment survey and finds most participants aged 20–35, gendered childcare responsibilities limiting job search, and financial status correlating with employment outcomes. Presents code snippets and plots, recommends targeted Instagram outreach and flexible scheduling, and warns that storytelling must validate analyses and label visuals for reproducibility.
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chapters (7)
- 0:00Introduction to storytelling
- 3:13Data science basics
- 7:28Feature engineering explained
- 13:14Case study insights
- 17:30Storytelling pitfalls
- 21:08Business impact of insights
- 23:25Q&A and closing
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