Data Science for Teens

Yes, the basics: variables, loops, functions, and lists. Month 1 includes a structured refresh, but it assumes prior exposure rather than teaching Python from zero. Students without that background should take our Python for Teens course first; it is the natural on-ramp, and many students move straight from one into the other.

Comfortable school maths is enough. Every statistical idea the course uses, means and medians, spread, percentiles, correlation, is taught inside the course with real data, and there is no calculus anywhere. The emphasis is on judgment: knowing when an average misleads matters far more here than deriving formulas.

Real, at an honest first level. Students train linear regression and classification models on real datasets with scikit-learn, use proper train-test splits, read confusion matrices, and meet overfitting directly. What we deliberately avoid is black-box theatre: no pasting magic code that scores well unexplained. A student finishing this course can tell you what their model learned and how wrong it typically is, which is more than many adults using these tools can.

Real public data throughout: movie databases, sports seasons, city open data such as street trees and road accidents, climate records, and the Palmer Penguins dataset for classification. From month 3 the data arrives genuinely messy on purpose, because cleaning real data is the core professional skill. For the capstone, students choose their own dataset around a question they care about.

Each student picks a question, finds a suitable public dataset, and does the whole job: cleaning with a documented log, exploratory analysis, honest figures, an optional model if it genuinely helps, and a written conclusion with a limitations section. The finished notebook is published to GitHub and presented live. It is a genuine piece of analysis with a public address, not a classroom-only exercise.

Everything is free. The course runs in Google Colab, which works in the browser, so pandas, matplotlib, and scikit-learn require no installation and no powerful computer. Any reasonably recent laptop with a stable internet connection is enough.

Live one-hour online classes, twice a week, in small groups capped at 10 students. Students work in their own notebooks during class rather than watching, and homework notebooks come back with written feedback. Mini-batch (3 to 4 students) and personal 1-on-1 options are available for students who want closer attention.

₹1,499 per month for group classes with 2 live classes weekly. Mini batches of 3 to 4 students are ₹2,499 per month, and personal 1-on-1 classes are ₹4,999 per month. International students pay $40 per month for group classes and $100 per month for 1-on-1.

The most common next step is our AI and machine learning track, which starts exactly where the scikit-learn month ends. Students leaning toward engineering deepen their Python with our advanced courses, and many put the skills straight to work in school science fairs and statistics projects. The capstone notebook also carries weight on its own as evidence of real analytical work for college applications.