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.
Recommended Challenges
· Uncertainty Quantification Clear- All
- Data Analysis
- Experimental design
- Simulation
- Exploratory Data Analysis
- Statistical Analysis
- Uncertainty Quantification
- Logistic regression
- Cost Modeling
- Hypothesis Testing
- Monte Carlo Simulation
- A/B testing with statistical significance
- Linear Regression
- Time series basics
- Bayesian methods
- Causal inference
- Sampling Methods
- CodeBeginnerNew
Predict Catalyst Properties for a Green-Hydrogen Pharma Spinout
Use an open catalyst dataset (e.g., Open Catalyst Project subset, or a Materials Project pull) where each candidate has descriptors and a target activity property. Train a tabul…
- Tabular Modeling
- Uncertainty Quantification
- Feature Engineering
AI for Science and Engineering - CodeSeniorNew
Triage Brain-CT Stroke Detector with Calibrated Uncertainty
You receive a curated public head-CT dataset (about 2,800 scans, slice-level labels for hemorrhagic stroke) and a held-out 600-scan hospital cohort. Train a 3D CNN or 2.5D slice…
- Medical Imaging
- Neural Networks
- Uncertainty Quantification
Machine Learning for Imaging and Medical Image Analysis - CodeIntermediateNew
Gaussian Process Regression for Wind Farm Power Curves
You receive 12 months of 10-minute SCADA data (wind speed, air temperature, power output) for 30 representative turbines, plus the manufacturer's published curve. Fit a GP with …
- Gaussian Processes
- Kernel Methods
- Uncertainty Quantification
Probabilistic Machine Learning - ResearchSeniorNew
Benchmark Conformal Prediction for a Healthcare Risk-Score
You receive a labeled dataset of about 25,000 patient encounters with the current risk-score's predictions and ground-truth 1-year outcomes. Implement and compare split conforma…
- Conformal Prediction
- Uncertainty Quantification
- Calibration
Statistical Machine Learning Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisIntermediateNew
Simulate Hospital Bed Allocation for a Healthtech Decision Support Pilot
You receive 12 months of anonymized admissions and discharges data plus ward layouts (medicine, surgery, ICU, geriatrics) and a small set of clinical transfer rules. Build a dis…
- Discrete Event Simulation
- Simpy
- Policy Comparison
Decision Support Systems and Decision Analysis - ResearchSeniorNew
Probabilistic Numerics for an ODE-Constrained Battery Model
You receive 12 months of charge/discharge cycle data for 50 battery packs from a delivery-van fleet, plus the existing single-particle ODE degradation model (Python). Use a prob…
- Probabilistic Numerics
- Bayesian Inference
- Ode Modeling
Probabilistic Machine Learning - CodeBeginnerNew
Calibrate a Demand Forecast with Bayesian Confidence Intervals
You receive 24 months of weekly demand for 600 SKUs plus the existing XGBoost point predictions. Fit a Bayesian conformal-prediction layer (or, alternatively, a Gaussian-Process…
- Bayesian Inference
- Uncertainty Quantification
- Conformal Prediction
Probabilistic Machine Learning - ResearchSeniorNew
Certify Robustness for a Medical-Imaging Classifier
You receive the classifier (a PyTorch ResNet variant) and a 4,000-image labeled validation slice. Apply randomized smoothing (Cohen et al.) at sigma in {0.25, 0.5, 1.0}. Report …
- Certified Robustness
- Randomized Smoothing
- Formal Verification
Trustworthy AI, Robustness, and Safety - Browse challenges
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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.
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.
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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.