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
· Simulation 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
- AnalysisIntermediateNew
Resilience Analysis of a National Power-Distribution Network
Receive an anonymized topology of the medium-voltage network (4,200 nodes, 4,800 edges, each edge with capacity + age + redundancy flag). Build the network in NetworkX, compute …
- Network Science
- Graph Analysis
- Resilience Analysis
Network Science and Computational Social Science - CodeBeginnerNew
Optimize Wind-Turbine Layout with a Genetic Algorithm
You receive a wind-speed-and-direction time series for the lease area, the polygon boundary, a minimum inter-turbine spacing constraint, and a Jensen wake model. Implement a rea…
- Genetic Algorithms
- Metaheuristics
- Constraint Handling
Evolutionary Computation and Metaheuristic Search - CodeSeniorNew
Plan Under Uncertainty for a Warehouse Restocking Robot
You receive a discrete-event simulator of a 1,200-shelf warehouse with calibrated optical-scanning error rates and stock-out cost per shelf. Formulate the restocking decision as…
- Planning Under Uncertainty
- Pomdp
- Monte Carlo Planning
Advanced Robotics - CodeIntermediateNew
Plan Inventory Replenishment as an MDP for an E-Commerce AI Startup
You receive 18 months of daily demand for 50 representative SKUs at one warehouse plus lead-time and unit-cost data. For one SKU at a time, formulate an MDP with state = (on-han…
- Mdp Modeling
- Value Iteration
- Dynamic Programming
Decision Making Under Uncertainty 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
- AnalysisBeginnerNew
Cache Configuration Study for a Memory-Bound Workload
Profile the existing inner loop on a workstation with perf to baseline L1/L2/L3 miss rates and miss latencies. Run the same loop through gem5's classic cache model under 6 confi…
- Caches
- Memory Subsystems
- Performance Modeling
Computer Architecture - 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 Or Tensorflow
Deep Reinforcement Learning - CodeSeniorNew
Grounded Language for a Robotics Pick-and-Place Demo
Use a tabletop simulator (PyBullet or Isaac Sim, both open) with 5 object types and 5 spatial relations (left of, right of, behind, in front of, on top of). Curate or generate a…
- Grounded Language Understanding
- Semantic Parsing
- Perception
Computational Semantics - CodeIntermediateNew
Prescriptive Route Optimization for a Sustainable Fashion Logistics Firm
Your team must develop a decision-support tool that recommends optimal delivery routes for EcoThreads' fleet. You'll need to model the logistics network, incorporate constraints…
- Optimization
- Simulation
- Route Planning
Business Analytics - 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
Temporal Planner for a Robotics Mission Operator
You receive 30 days of mission logs with task lists, time windows, and actual durations. Encode the planning problem with temporal PDDL (PDDL 2.1 durative actions) and solve wit…
- Temporal Planning
- Pddl Modeling
- Simulation
Automated Planning - ResearchSeniorNew
Embodied Visual Reasoning for a Warehouse Pick Assistant
Use an embodied simulator (Habitat 3.0 or Isaac Sim — pick one and justify) to render 300 cluttered-bin scenarios with a target item label. For each scenario, build two reasonin…
- Embodied Vision
- Vision Language Models
- Visual Reasoning
Visual Intelligence and Visual Reasoning - DesignSeniorNew
Auction Design for Renewable Energy Subsidies
Your team must design an auction mechanism for allocating subsidies to offshore wind projects. You have historical bid data from past auctions, cost estimates for different tech…
- Auction Theory
- Mechanism Design
- Game Theory
Industrial Economics and Game Theory - CodeIntermediateNew
Run a Monte Carlo Tree Search Strategy for a Robotics Pick-and-Place Task
You receive a simulator of the pick-and-place task: a bin with 10 randomly-placed parts, an action space of which part to pick next, and a reward = parts picked per minute with …
- Monte Carlo Tree Search
- Planning
- Simulation
Decision Making Under Uncertainty 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
Planning Under Uncertainty for a Last-Mile Delivery Fleet
Build a simulator of the 50-block area with stochastic travel times conditioned on weather and time-of-day. Implement value iteration (for a small state space), MCTS (Monte Carl…
- Planning Under Uncertainty
- Markov Decision Processes
- Monte Carlo Tree Search
Automated Planning - CodeBeginnerNew
Tabular Q-Learning for Warehouse Slotting
You receive a Python discrete-event simulator with state encoded as a 12-dimensional categorical vector (around 8,000 reachable states) and 6 possible slotting actions, plus 2 y…
- Tabular Rl
- Q Learning
- Epsilon Greedy
Reinforcement Learning - CodeBeginnerNew
Behavior Cloning for a Pick-and-Place Manipulator
You receive 200 human teleoperated demonstrations (state + action trajectories) of picking 8 small electronic components from a tray and placing them at marked locations in a ro…
- Behavior Cloning
- Imitation Learning
- Manipulation
Robot Learning - DesignIntermediateNew
Design a Negotiation Protocol for Trading Agents
Choose a negotiation framework (alternating-offers Rubinstein, monotonic concession, or auction-based) and justify against the freight use case. Implement a simulator in Python …
- Agent Negotiation
- Game Theory
- Multi Agent Systems
Multi-Agent Systems - DesignBeginnerNew
Simulation-Based Capacity Planning for a 40-Person SaaS Scale-Up
Your task is to simulate Flowly's customer success operations over the next 12 months. Model the arrival of new enterprise customers, their onboarding and support requests, and …
- Simulation
- Capacity Planning
- Sensitivity & Scenario Analysis
Business Analytics - CodeIntermediateNew
Designing a Dynamic Pricing Engine for a Ride-Hailing Startup
Your team is given a dataset of historical rides (timestamp, pickup location zone, demand level, available drivers). You must design a pricing algorithm that: (1) uses a multipl…
- Python Or Javascript
- Pandas
- Simulation
Programming for Business Applications - DesignSeniorNew
Dynamic Pricing Optimization for a Ride-Hailing Platform
You are a data scientist at CityRide. Using 6 months of historical trip data (pickup/dropoff, time, fare, surge multiplier), weather data, and local events calendar, you must bu…
- Reinforcement Learning
- Optimization
- Simulation
Data Science for Business - CodeIntermediateNew
Simulating Queueing for a 40-Person SaaS Support Team
Build a discrete-event simulation of the ticket handling process: tickets arrive randomly (Poisson), are triaged, then assigned to specialists (tier 1 and tier 2). Calibrate usi…
- Simulation
- Queueing Theory
- Python Or Javascript
Operations Analytics and Optimization - ResearchSeniorNew
Solve a POMDP for a Healthtech Diagnostic Pathway
You receive a simplified pathway: 5 possible underlying conditions, 8 possible diagnostic tests each with documented sensitivity and specificity, and an outcome payoff matrix fr…
- Pomdp Modeling
- Belief States
- Approximate Solvers
Decision Making Under Uncertainty - CodeIntermediateNew
Safety-Critical Test Harness for an AV Planner
Use CARLA (open-source AV simulator) and encode 10 representative safety scenarios across 3 categories (cut-in, pedestrian emergence, signalized-intersection right-of-way). Writ…
- Simulation
- Scenario Testing
- Safety Evaluation
AI for Autonomous Vehicles - CodeIntermediateNew
Use Actor-Critic to Auto-Tune a HVAC Control Policy
You receive a Sinergym wrapper around the EnergyPlus model of one floor with 8 thermal zones, weather data for one year, and occupancy schedules. Train a Soft Actor-Critic (SAC,…
- Actor Critic
- Soft Actor Critic
- Continuous Control
Deep Reinforcement Learning - AnalysisSeniorNew
Stochastic Inventory Policy for a Sustainable Fashion Brand
Using historical demand data (provided), fit a demand distribution and determine optimal (s, S) or (R, Q) policy parameters. Consider perishability (seasonal collections) and a …
- Inventory Optimization
- Stochastic Modeling
- Simulation
Operations Analytics and Optimization
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
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Sponsor a challenge and meet candidates through actual work.
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