Computer Science
Programming Fundamentals Challenges
Programming Fundamentals challenges put you inside the work of writing code that is correct, readable and fast. You'll build core skills in functions & data structures, object-oriented design and design patterns, work in Python or JavaScript, and learn to do code reading and refactoring the way teams expect.
From there you'll tackle the harder edges — algorithm analysis, complexity analysis, graph algorithms and generics & type systems — pushing into performance engineering, low-latency programming patterns and systems-language proficiency (Go, Rust, C++). Each challenge you solve earns a verified credential you can share with recruiters.
- AnalysisSeniorNew
Amortized-Analysis Investigation of a Production Telemetry Cache
Working only from the three provided materials — the de-identified incident record (incident-record), the representative cache source module (cache-source), and the recorded ope…
- Amortized Analysis
- Data Structures
- Algorithm Analysis
Open coursework - CodeIntermediateNew
Diagnose Equipment Failures with a Bayesian Network
You receive 90 days of sensor logs (vibration, spindle temperature, coolant flow, ambient humidity), the maintenance log of 180 failure events labeled by root cause, and a short…
- Bayesian Networks
- Probabilistic Inference
- Parameter Learning
Probabilistic Graphical Models - ResearchIntermediateNew
Quantify Distribution Shift for a Climate-Risk Model
You receive the model artifact (a gradient boosted regressor predicting expected annual loss per property), 2010-2020 training data, and a 2021-2024 holdout. Quantify covariate …
- Distribution Shift
- Covariate Shift
- Concept Drift
Trustworthy AI, Robustness, and Safety - AnalysisBeginnerNew
Mine Association Rules for a Grocery Retailer's Promo Strategy
You receive 6 months of basket-level transaction data (around 22 million baskets, around 18,000 SKUs) plus a category taxonomy. Run association-rule mining (Apriori or FP-Growth…
- Association Rules
- Market Basket Analysis
- Apriori
Data Mining and Knowledge Discovery 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
- CodeBeginnerNew
Open-Domain QA over Product Documentation
You receive a snapshot of the documentation (Markdown) and 120 real support questions with the URLs of pages containing the answer. Build an open-domain QA pipeline: chunk the d…
- Open Domain Qa
- Passage Retrieval
- Reading Comprehension
Question Answering and Conversational Systems - AnalysisIntermediateNew
Build a Bayesian Credit-Scoring Model for an Emerging-Markets Fintech
You receive an anonymized snapshot of about 30,000 historical applications with features (income proxy, tenure on platform, prior loans, region) and the binary default outcome. …
- Bayesian Learning
- Credit Scoring
- Model Evaluation
Advanced Machine Learning - CodeBeginnerNew
Build a Bounded Concurrent Queue for a Microservice Worker Pool
Implement a bounded concurrent queue in Go (no third-party queue libraries; standard library + sync primitives only) that supports: Put(item) blocks when full, TryPut(item, time…
- Concurrent Data Structures
- Mutex And Condvar
- Go
Concurrent and Parallel Programming - DesignIntermediateNew
Visualize Embedding Drift for a RAG Knowledge Assistant
You receive weekly snapshots over 12 weeks of around 50,000 document embeddings each (1024-dim). Design and build a visualization tool that: (a) projects each snapshot to 2D wit…
- Embeddings
- Dimensionality Reduction
- Umap
Data Visualization - 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
Code Review and Refactoring of a Legacy E-Commerce Module
You are given a codebase (Java/Spring Boot) of the order module. Perform a static analysis, document issues (e.g., god classes, tight coupling), and write a refactoring proposal…
- Code Review
- Refactoring
- Java
Software Engineering and Quality Assurance - AnalysisBeginnerNew
Audit a Climate-Tech Sensor Dataset for Production Readiness
You receive 18 months of raw sensor readings from 1,200 sensors (about 800M rows), plus a sensor-metadata table (location, firmware version, deployment date). Profile the data f…
- Data Quality Audit
- Data Profiling
- Time Series Analysis
Applied Data Analysis and Practical Data Science - AnalysisBeginnerNew
Audit a Hiring-Screening Model for Demographic Bias
You receive: (a) inference API access to the production model (black-box), (b) a 12,000-resume audit benchmark with self-declared gender and age-band labels (consented, GDPR-com…
- Fairness Metrics
- Bias Auditing
- Model Evaluation
AI Ethics, Fairness, and Responsible AI - CodeBeginnerNew
Simulated Annealing for Shift Scheduling at a Hospital
You receive 6 months of anonymized shift demand data, the nurse roster (skills, certifications, contracted hours), and the labor-law hard constraints. Encode the schedule as a 7…
- Simulated Annealing
- Metaheuristics
- Constraint Handling
Evolutionary Computation and Metaheuristic Search 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
- CodeIntermediateNew
Index a Reference Genome with a Suffix Array in Rust
Using the reference-genome FASTA in the provided materials, build an in-memory suffix array (DC3 or SA-IS construction) over the 4-letter DNA alphabet in Rust, then implement co…
- Suffix Arrays
- String Algorithms
- Rust
Open coursework - CodeSeniorNew
Build a Unikernel for a Privacy-Focused VPN Provider
Choose either MirageOS or Unikraft as the unikernel framework. Build an image running a minimal WireGuard data plane (existing OCaml or Rust WireGuard reference implementation i…
- Unikernels
- Virtualization
- Wireguard
Advanced Operating Systems - CodeIntermediateNew
Implement Model Predictive Control for a Delivery Robot
You receive a kinematic bicycle model of the robot, a published track layout, and 30 minutes of recorded waypoint trajectories. Implement a nonlinear MPC controller using acados…
- Model Predictive Control
- Optimal Control
- Robotics Simulation
Advanced Robotics - CodeFoundationalNew
Optimizing Inventory for a Barcelona D2C Cosmetics Brand
You are given a CSV file with 6 months of daily sales data for 20 SKUs, including product name, date, units sold, and current stock level. Your task is to write a Python program…
- Python
- Data Cleaning
- Data Analysis
Programming for Business Applications - AnalysisIntermediateNew
Frame an Energy-Storage Dispatch Decision as a Bayesian Decision Problem
You receive 2 years of hourly spot-price data, 2 years of wind generation data, and a manufacturer's battery degradation model. Frame dispatch as a Bayesian decision problem: mo…
- Bayesian Decision Theory
- Price Modeling
- Back Testing
Decision Making Under Uncertainty - 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 - CodeIntermediateNew
Multi-Tenant Schema Migration for a Series-B HR SaaS
Design a 4-phase migration: (1) add new columns + tables, dual-write on every workflow mutation, (2) backfill 3.4 TB in chunks of around 50k rows per minute with throttling and …
- Multi Tenant Architecture
- Schema Migration
- Dual Write
Engineering Software as a Service - CodeIntermediateNew
Build an End-to-End ML Pipeline for Loan-Default Prediction
You receive 24 months of historical application + outcome data (about 380,000 rows). Build a pipeline using a workflow orchestrator (Prefect, Kedro, or a simple Makefile chain) …
- Ml Pipelines
- Feature Engineering
- Pipeline Testing
Machine Learning in Practice - CodeFoundationalNew
Diagnose and Rebuild a Slow Recipe Search at a Marketplace Startup
Work only from the three items in the provided materials: the current search function, a representative sample of the recipe catalog, and the fixed set of representative queries…
- Algorithm Analysis
- Big O
- Data Structures
Open coursework - CodeFoundationalNew
Tune a Pick-and-Place Controller for a Cosmetics Co-Packer
You receive 4 hours of logged trajectories from the existing controller (joint positions, target poses, miss/success labels) and read/write access to the controller config (YAML…
- Motion Control
- Trajectory Tuning
- Robot Kinematics
Robotics - ResearchIntermediateNew
Disease-Progression Modelling for a Neurodegeneration Biotech
You receive a curated longitudinal Parkinson's cohort (about 1,200 patients, 4-12 visits each, MDS-UPDRS sub-scores, cognitive assessments, demographics). Fit (1) a linear mixed…
- Disease Progression Modeling
- Mixed Effects Models
- State Space Models
Machine Learning for Healthcare and Biomedicine - DesignIntermediateNew
Build a Feature Store for a Fintech Fraud Team
You will design a feature-store layer covering 12 representative fraud features (account-level, merchant-level, transaction-level), with both batch (Spark) and online (low-laten…
- Feature Stores
- Data Pipelines
- Spark
Machine Learning at Scale
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.
Related skill families
Browse all skillsIndustry 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.