Femanalytica-DataCamp Projects

The Netflix, Airbnb, and NYC schools projects have the most concrete findings. Netflix: filtering movies from the 1990s, the most common runtime was 94 minutes, with 7 action films clocking in under 90, short for the genre. Airbnb: Manhattan and Brooklyn have almost the same number of listings, around 10,300 each, but Manhattan's average price sits at 184 dollars against Brooklyn's 122. Sorting neighborhoods by average price alone is misleading too, Sea Gate topped the list at 805 dollars average, but that's from 2 listings. Tribeca, with 61 listings at 397 average, is the actual top of the market once you check sample size against the headline number. NYC schools: across 375 schools, the average total SAT score was 1276, but the spread ran from 924 to 2144, more than double between the lowest and highest performer. Staten Island had the highest average by borough, ahead of Queens and Manhattan, which doesn't match the pattern you'd expect if you assumed borough wealth or size predicts school performance the same way it predicted Airbnb pricing.

The remaining three were narrower in scope by design: building a user registration function with proper input validation and error handling, prepping a customer dataset for modeling through imputation and encoding, and charting a century of Nobel Prize demographics.

None of these were meant to produce a novel insight. They were the reps: 40-plus courses' worth of technique, applied to real datasets instead of toy examples.