One typo, opposite answer
DataFest wrapped on Sunday. Thank you to everyone who learned, built, and climbed the leaderboard this past week.
This issue starts with a plain-English guide to data science, where a single typo in ten days of sales notes flips the answer completely. AI can calculate an answer in seconds. Knowing whether the data and conclusion hold up is the skill Dataquest helps you build.
Here's what's inside:
Data science is using data to answer questions and make better decisions. This guide shows what that looks like in practice with one small project: a friend's coffee cart, ten days of high temperatures and iced drink sales, and one question. Do hotter days go with more iced drinks sold?
The notes hide two problems: a blank Sunday and a sales figure of 240 on a cool Tuesday. Fix the typo (it was 24) and drop the blank day, and the correlation is 0.99. Leave the typo in, and it comes out at −0.29, pointing the wrong way entirely.
Why it matters: AI tools can write the code for every step. Catching the 240 and knowing that nine days can't prove cause and effect is still on the person reading the result. The guide also covers how data science differs from AI, analytics, and machine learning, and where to start learning.
The DataFest Code Challenge is a wrap.
Thank you to every learner who joined in, whether you finished one lesson or a whole course. Congratulations to the three learners at the top of the leaderboard:
A chart can be technically correct and still say nothing. In this live session, we'll take Euro exchange rate data, clean it, smooth the noise with a rolling mean, then find a story angle and sketch the graph before writing a line of Matplotlib.
The design decisions are the point: what to highlight, what to cut, and how information design and pre-attentive attributes guide the reader's eye. You'll leave with a portfolio-worthy visualization.
GUIDED PROJECT · PYTHON · MATPLOTLIB · INTERMEDIATE
Classify patients as high or low heart disease risk using anonymized medical data. In healthcare, a false negative has real consequences, so the trade-off between precision and recall stops being abstract fast.
Skills and tools you'll practice: KNN, scikit-learn, pandas, confusion matrices, precision and recall.
Take it further: compare KNN against logistic regression and random forest. A model comparison section makes a much stronger portfolio piece than a single model in isolation.
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Hooi is working through the Zero to GPT skill path and would appreciate help with the calculations behind backpropagation. Know your gradients? Jump in.
Upgrade a SQL-on-files project in a copy, test the new VARIANT type and async I/O, and document what changes.
Map natural forests, plantations, and tree crops at 10-meter resolution, then measure land-use change over time.
Run the open SQLite-based test suite to compare coding agents across languages and see how reproducible, behavior-based evaluations are designed.
Use the monthly dashboards on AI exposure, hiring, and adoption to write an evidence-based career or labor-market brief.
Port a small data project and test lazy imports, UTF-8 defaults, frozendict, the new profiler, and the typing and free-threading updates.
Learning sticks better with company.
Know someone who'd like to build data skills alongside you? Refer a friend to Dataquest and they'll get 20% off while you get $20. Refer a friend
No renewals. Just progress.