AI & Data
Statistics & Data Science Methods Challenges
Statistics & Data Science Methods challenges put you inside the work of drawing trustworthy conclusions from data. You'll build Statistics Fundamentals and Statistical Analysis, run Exploratory Data Analysis, Hypothesis Testing, Confidence Intervals, and Linear Regression, and design clean Sampling Methods.
From there you'll handle the harder edges — Bayesian methods, Causal inference, A/B testing with statistical significance, Monte Carlo Simulation, and Uncertainty Quantification — applying Experimental design the way data scientists actually do. Each challenge you solve earns a verified credential you can share with recruiters.
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- AnalysisBeginnerNew
Design a Churn-Prediction and Retention Model for TaskFlow
Using the TaskFlow customer dataset and the VP's framing memo (both provided), design and document a churn-prediction model. Perform exploratory analysis to surface patterns and…
- Logistic Regression
- Classification
- Feature Engineering
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Predict 30-Day Churn for a Direct-to-Consumer Cosmetics Brand
Working only from the two materials you are given (a subscriber behavior sample dataset and a one-page stakeholder brief from the head of retention), build a binary classificati…
- Python
- Scikit Learn
- Logistic Regression
Open coursework - CodeBeginnerNew
Predict Subscription Churn for an EdTech Platform
You receive a CSV with about 18,000 student-month rows: features include login frequency, session length, quiz scores, parent app opens, and plan tier. The target is whether the…
- Supervised Learning
- Logistic Regression
- Gradient Boosting
Machine Learning (Undergraduate) - DesignIntermediateNew
Design a Churn Prediction and Retention Proposal for ConnectTel
Using the ConnectTel customer sample (the provided dataset) and the ConnectTel retention context brief, design a proposal for predicting which customers are most likely to leave…
- Logistic Regression
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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
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Lead-Scoring Model and AI Roadmap for an Enterprise-Bound SaaS Firm
Using the Enterprise Trial History dataset, build a binary classification model that predicts whether an enterprise trial converted to a paid contract. The model must be explain…
- Lead Scoring
- Logistic Regression
- Ai Strategy
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Stress-Test a Hiring-Funnel Model for Bias
You receive a synthetic-but-realistic dataset of 25,000 past applicants with features (years of experience, education tier, prior role tags) and outcome labels (advanced past th…
- Model Evaluation
- Fairness Metrics
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Machine Learning (Undergraduate)
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.