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
· Experimental design 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
- ResearchIntermediateNew
Recommend a Color Scale for a Flood-Risk Map With Evidence
Working only from the provided materials, reach and defend a single recommendation. Read the decision file (decision-file) to understand the actors, the prior complaint, and the…
- Perceptual Study
- Color Scales
- Experimental Design
Open coursework - ResearchSeniorNew
Stress-Test Scalable Oversight on a Tool-Using Agent
Design a sandwich-oversight study: pick a task domain where non-expert oversight is plausible but not trivial (e.g., reviewing data-analysis steps, checking small bug fixes, eva…
- Scalable Oversight
- Alignment Research
- Experiment Design
AI Safety and Alignment - CodeIntermediateNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch
Open coursework - ResearchSeniorNew
Reproduce a Mechanistic Interpretability Result on a Small Transformer
Pick a published mechanistic-interpretability paper that operates on a small (under 1 billion parameter) open-source transformer (e.g., GPT-2 small, Pythia 70M). Set up the envi…
- Mechanistic Interpretability
- Transformer Internals
- Pytorch
AI Safety and Alignment 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
- StrategyBeginnerNew
Pitch a Regulatory Sandbox Application for an Edtech AI Product
Read the EU AI Regulation's regulatory-sandbox provisions. Pick a member-state sandbox program (Spain, Norway-as-EEA, or a German-state pilot are publicly documented options) an…
- Regulatory Analysis
- Ai Governance
- Product Strategy
Open coursework - ResearchIntermediateNew
Train a NeRF for Real-Estate Virtual Tours
You receive a curated dataset of 3 apartments, each with around 120 input images and known camera poses (already SfM-processed). Train a NeRF variant (Instant-NGP or Nerfacto re…
- Neural Scene Representation
- Nerf
- Pytorch
Open coursework - ResearchSeniorNew
Train Cooperative Agents with Multi-Agent RL
Pick an open multi-agent environment (PettingZoo's MPE 'simple_spread', Overcooked-AI, or SMAC). Implement or wrap three methods: IPPO (independent PPO per agent), MAPPO (centra…
- Multi Agent Reinforcement Learning
- Ppo
- Pytorch
Open coursework - ResearchBeginnerNew
Evaluate a Generative AI Image Tool with a Within-Subjects Study
You will write a study protocol, recruit 20 participants (a Discord callout is fine), counterbalance the two conditions, and run 45-minute sessions over Zoom. Collect three meas…
- Experiment Design
- User Study
- Within Subjects Design
Human-Computer Interaction for AI Systems - 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.
- DesignBeginnerNew
A/B-Test a Recommender Improvement Without Breaking Trust
You receive offline-evaluation results for both the production and candidate models plus aggregate metrics from the last 12 weeks (recipe views, save rate, weekly active users, …
- Experiment Design
- Ab Testing
- Metric Design
Machine Learning in Practice - ResearchSeniorNew
Benchmark Reward-from-Feedback Methods on a Tabletop Pick-Place
You will use a Franka Panda arm in PyBullet on a 4-object pick-and-place task. For each of the three feedback methods, train a reward model and a downstream policy until converg…
- Reinforcement Learning
- Reward Learning
- Preference Comparison
Human-Robot Interaction - ResearchIntermediateNew
Reproduce a Vision-Model Paper Under a Reproducibility Standard
Pick a vision-model paper from CVPR or NeurIPS 2024-2025 with publicly available code and a manageable compute footprint (single-GPU under 24 hours). Reproduce the headline metr…
- Reproducibility
- Experimental Design
- Model Evaluation
AI Measurement and Evaluation - ResearchSeniorNew
Compare RNN vs Transformer for Long-Sequence Modeling
Pick a public trajectory dataset (e.g., Argoverse 2, Waymo Open, or ETH-UCY). Implement three models with comparable parameter counts (around 5M each): an LSTM baseline, a vanil…
- Transformers
- Rnn
- State Space Models
Open coursework Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- ResearchIntermediateNew
Hands-on Lab: Reproduce a Recent SOTA Vision Paper
Pick one of three pre-approved 2025 papers (offered by the supervisor) with a known reference codebase you may consult but not copy. Re-implement the model and training loop in …
- Pytorch
- Paper Reproduction
- Experiment Design
Open coursework - ResearchSeniorNew
Experimental Design for a Fintech App's Savings Nudge
You are a behavioral data scientist at SaveSmart. Design a randomized controlled trial (RCT) to test the effect of a 'future self' nudge on savings behavior. Define treatment an…
- Experimental Design
- Statistical Analysis
- Nudge Theory
Behavioral Economics - ResearchSeniorNew
Pre-Register and Run a Small Neural-Network Ablation Study
You will study how three architectural and regularization choices (depth: 2/4/8 hidden layers; activation: ReLU vs. GELU; weight decay: 0 / 1e-4 / 1e-3) affect a small MLP's tes…
- Neural Networks
- Regularization
- Experiment Design
Open coursework - ResearchSeniorNew
Neuromarketing Audit for a Sustainable Fashion Retailer
Your team will design and simulate a neuromarketing experiment. First, propose two alternative store layouts (e.g., circular vs. grid) and two website homepage designs (e.g., he…
- Neuromarketing
- Experimental Design
- Eye Tracking Analysis
Consumer Behavior - ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Open coursework - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - CodeSeniorNew
Auto-Tune a Distributed Training Cluster's Throughput
Pick a representative fine-tune job (an open 7B model on a public instruction dataset is fine). Define the search space: NCCL_ALGO, NCCL_PROTO, num_workers, prefetch_factor, gra…
- Distributed Training
- Hyperparameter Tuning
- Nccl
Machine Learning Systems - ResearchSeniorNew
SAT-Based Planner for Smart-Grid Demand Response
Encode the dispatch problem (which customers to curtail by how much, respecting per-customer contractual caps and grid-cell totals) as a SAT or MaxSAT instance. Solve 50 histori…
- Sat Based Planning
- Constraint Encoding
- Benchmarking
Automated Planning - ResearchSeniorNew
Long-Context QA Evaluation Benchmark for Legal Memoranda
You receive 25 anonymized legal memoranda (50-90 pages each) and 100 QA pairs whose answers are deliberately spread across the documents (25 in pages 1-20, 25 in pages 20-40, 25…
- Long Context Qa
- Benchmark Design
- Model Evaluation
Open coursework - ResearchIntermediateNew
Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Scope the problem yourself (suggested examples: sentiment classification on a niche domain, tabular anomaly detection, time-series forecasting on a public dataset). Define the t…
- Experiment Design
- Statistical Testing
- Pytorch
Open coursework - ResearchBeginnerNew
Run a Human-Preference Study Comparing Two Coding Assistants
Design a blinded paired-comparison study: 12 developer participants, each gets the same 8 realistic coding tasks (refactor, write a function, debug, test), each task is solved b…
- Experiment Design
- Statistical Evaluation
- Human Evaluation
AI Measurement and Evaluation - ResearchSeniorNew
Quantify Sim-to-Real Gap for a Warehouse Manipulation Policy
You receive a trained pick-and-place policy (PyTorch), the simulation env (Isaac Lab), and access to a real-arm rig (or recorded teleop episodes if hardware is unavailable). Def…
- Sim To Real
- Manipulation
- Experiment Design
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.
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