Overview
What this challenge is about.
Build a forgetting-curve model on 8,000 learner logs, simulate smarter review schedules, and compare to a hand-tuned baseline. Get a verifiable certificate.
The scenario
The startup (around 20 people, late-seed, around 350k monthly active learners across India and Southeast Asia) competes with global incumbents on price and any meaningful retention lift translates to lower churn and meaningful runway extension.
The Brief
What you'll do, and what you'll demonstrate.
Build a personalized forgetting-model-based spaced-repetition engine that beats the hand-tuned baseline on retention-per-effort.
Earning criteria — what you'll demonstrate
- Apply forgetting-curve models (HLR, half-life regression) to real behavioral data
- Personalize learning algorithms with per-learner parameters
- Run offline simulation as a substitute for online A/B in early stages
- Design an A/B test for a retention-sensitive product
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Spaced Repetition
Apply spaced repetition to solve real industry problems and demonstrate production-level capability.
- Personalization
Apply personalization to solve real industry problems and demonstrate production-level capability.
- Behavioral Data
Apply behavioral data to solve real industry problems and demonstrate production-level capability.
- Regression Modeling
Apply regression modeling to solve real industry problems and demonstrate production-level capability.
- Ab Testing Design
Apply ab testing design to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Personalized scheduling models with offline simulation + A/B test plans are the MLE-shaped half of edtech recommendation systems.
This challenge sharpens
- personalization
- spaced-repetition
- regression-modeling
Data Scientist
Behavioral-data modeling with retention-per-effort framing is the daily data-science work at consumer-edtech companies.
This challenge sharpens
- behavioral-data
- regression-modeling
- ab-testing-design