A/B Testing
If you like applying A/B Testing, every challenge here gives you a chance to practice it on a real industry brief.
- AnalysisIntermediateNew
Measure HTTP/3 vs HTTP/2 Video Delivery Over Cellular
Using the architecture brief, the four-week quality-of-experience dataset, the cellular-and-video-profiles specification, and the starter synthetic-client harness module (all pr…
- Quic Http3
- Network Measurement
- Transport Protocols
Open coursework - DesignIntermediateNew
Design an Adaptive Home Screen for a Streaming Recommender
Using the recommender output specification and the session-signals fixture, design three adaptive layout variants that are each triggered by a viewing-intent signal derived from…
- Adaptive Interfaces
- Interaction Design
- A B Testing
Open coursework - AnalysisIntermediateNew
Design TaskFlow's Pricing-Page Experiment Verdict and Decision Proposal
Working only from the two provided materials — the visitor-level experiment dataset (`ab_test_visitors.csv`) and the VP decision brief (`vp_decision_brief.md`) — design a propos…
- A B Testing
- Statistical Analysis
- Bayesian Methods
Open coursework - AnalysisIntermediateNew
Mine Basket History to Justify Grocery Shelf-Adjacency Changes
Work only from the materials provided. Use the basket transactions extract to mine frequent itemsets with FP-growth, tuning the minimum support level separately for the food, ho…
- Frequent Itemset Mining
- Fp Growth
- Spark
Open coursework Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Build a Hybrid Recommendation System for an Indie Streaming Catalog
Use the provided 6-month anonymized event log (around 320M play events, 1.4M unique users in the held-out cohort), audio embeddings (256-d), and track metadata. Implement (1) an…
- Recommendation Systems
- Collaborative Filtering
- Content Based Recommendation
Open coursework - CodeIntermediateNew
Build a Canary Rollout for a Production Recommender
Pick a serving stack (Triton, Seldon Core, KServe, or BentoML). Implement two-model traffic splitting with a configurable percentage (start at 5%). Wire up online metric collect…
- Canary Deployment
- Kubernetes
- Ab Testing
ML Engineering and Production ML - AnalysisIntermediateNew
MCMC for Conversion-Funnel A/B Testing at a Marketplace
You receive 6 weeks of per-visitor funnel data (visit, sign-up, trial start, trial-to-paid conversion) split by variant and by acquisition channel (organic, paid social, paid se…
- Mcmc
- Bayesian Hierarchical Models
- Ab Testing
Open coursework - CodeIntermediateNew
Build a Hybrid Recommender for a Niche Consumer-AI Music App
You receive listening events (around 240 million plays) plus a content embedding per track (audio + curator tags). Build a collaborative filtering model (ALS or implicit-feedbac…
- Recommender Systems
- Collaborative Filtering
- Content Based Filtering
Data Mining and Knowledge Discovery - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Build a Forgetting-Curve-Aware Spaced-Repetition Engine
You receive 6 months of practice logs from 8,000 learners (item, timestamp, response correctness, response latency). Fit a learner-personalized forgetting model (logistic per-it…
- Spaced Repetition
- Personalization
- Behavioral Data
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.