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.
- CodeSeniorNew
Forecasting Exchange Rates for a 40-Person SaaS Scale-Up
Your task is to collect monthly data for the past 5 years for GBP/USD and EUR/USD, along with relevant predictors (interest rate differentials, inflation, GDP growth, trade bala…
- Time Series Forecasting
- Econometric Modeling
- Python
Open coursework - CodeBeginnerNew
Build a Source-to-Source Migration Tool for a Legacy API
Use Python's libcst (concrete syntax tree library) to parse customer code while preserving formatting. For each of the 14 transformations, write a CST visitor that matches the v…
- Abstract Syntax Trees
- Source To Source Transformation
- Python
Open coursework - AnalysisIntermediateNew
Design an Electronic Health Record Data-Quality Audit
Stand up a Python (pandas + DuckDB) audit notebook ingesting the 14M-record extract. Define and run quality checks across four dimensions: completeness (required-field missingne…
- Health Informatics
- Data Quality
- Snomed Ct
Computational Biology and Health Informatics - CodeBeginnerNew
Design a Domain Model for a Library Loan System (Java)
Implement, in Java 21, an Item type hierarchy (Book, AudioBook, Periodical, EquipmentLoan — each with sub-type-specific rules), Member with eligibility logic, Loan as an aggrega…
- Java
- Oo Design
- Domain Modeling
Object-Oriented Programming and Design Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- CodeIntermediateNew
Plan Warehouse Pick Routes with a Classical Planner
You receive a stylized warehouse map (aisle graph), 30 sample shifts of pick tasks, and the current heuristic's outputs. Write a PDDL domain + problem generator, solve with at l…
- Pddl Modeling
- State Space Search
- Classical Planning
Open coursework - 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
- Pandas
- Simulation
Programming for Business Applications - CodeIntermediateNew
Build a Feature Store Backbone for a Healthtech ML Team
You receive synthetic wearable telemetry (heart rate, accelerometer, sleep stages) for around 5,000 patients across 90 days, plus the existing scattered feature scripts from the…
- Feature Engineering
- Data Modeling
- Python
Data Engineering and Big Data Systems - AnalysisIntermediateNew
Macroeconomic Forecast for a Renewable Energy Investment Firm
You are a junior strategist at GreenCap. Using historical data (10+ years) from Eurostat and the Bank of Spain, develop a VAR or similar model to forecast GDP growth, electricit…
- Multivariate Time Series
- Var Modeling
- Scenario Analysis
Forecasting for Decision Making - Browse challenges
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Pricing Strategist
Set the price that captures value without leaving sales on the table. Demand modelling, willingness-to-pay research, and the disciplined experimentation that turns pricing into a competitive advantage.
- CodeIntermediateNew
Build an Anomaly-Detection Pipeline for Pharma Cold-Chain Logistics
You receive 18 months of shipment telemetry (around 60,000 shipments, around 12 million sensor readings) plus a hand-labeled set of 1,200 incidents (mix of true excursions, sens…
- Anomaly Detection
- Feature Engineering
- Time Series
Data Mining and Knowledge Discovery - CodeBeginnerNew
Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot
Model home-battery dispatch as a finite MDP: state is (state-of-charge, hour-of-day, current price tier), actions are charge/hold/discharge with realistic efficiency losses, tra…
- Markov Decision Processes
- Value Iteration
- Policy Iteration
Artificial Intelligence: Principles and Techniques - CodeSeniorNew
Rebuild a Fair Single-Selfie Avatar Pipeline at NorthRoom
Using the prior-attempt postmortem (provided), the cohort manifest (provided), the FairFace public image set (provided link), the FLAME face-model resource (provided link), and …
- Digital Humans
- Geometry Processing
- 3d Rendering
Open coursework - CodeIntermediateNew
Design an Anti-Corruption Layer for a Legacy Hospital EHR
Map the legacy EHR's HL7 v2 message schema (ADT, ORM, ORU segments) to the new platform's domain (Patient, Encounter, Order, Result). Identify the 6-10 anti-patterns in the lega…
- Anti Corruption Layer
- Hl7 Integration
- Domain Modeling
Domain-Driven Design Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
Why Ewance
- AnalysisBeginnerNew
Right-Size a Real-Time Recommendation Serving Cluster
You receive 7 days of request-level telemetry (timestamp, latency, error code, pod) plus the existing Horizontal Pod Autoscaler (HPA) and node-group configs. Analyze traffic pat…
- Model Serving
- Kubernetes
- Autoscaling
Machine Learning at Scale - DesignIntermediateNew
Design a Continuous Eval Pipeline for an Enterprise RAG Product
Design (and partially build) a continuous-eval pipeline for a RAG system: (1) a structured eval set with at least 50 queries grouped by query class; (2) automated scoring (LLM-a…
- Continuous Evaluation
- LLM Evaluation
- Retrieval Augmented Generation
AI Measurement and Evaluation - CodeIntermediateNew
Build a Forgetting-Curve-Aware Spaced-Repetition Engine
You receive 6 months of practice logs from 8,000 learners (item, timestamp, response correctness, response latency). Fit a learner-personalized forgetting model (logistic per-it…
- Spaced Repetition
- Personalization
- Behavioral Data
Open coursework - 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 - CodeIntermediateNew
Forecast Intraday FX Volatility for a London Liquidity Desk
You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …
- Time Series Forecasting
- Feature Engineering
- Model Validation
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 - AnalysisBeginnerNew
Audit a Hiring-Screen Classifier for Fairness Across Cohorts
You receive the classifier as a black-box API and a synthetic-but-realistic dataset of 8,000 CVs with imputed demographic proxies (gender, age band, regional cluster) and labele…
- Fairness Evaluation
- Disparate Impact
- Audit Methodology
Trustworthy AI, Robustness, and Safety - ResearchIntermediateNew
Build a Generalization-Bound Tutorial for an MLE Onboarding Track
You will produce a Jupyter-notebook tutorial covering (1) sample-complexity intuition, (2) VC-dimension with worked examples for halfspaces and decision stumps, (3) Rademacher c…
- Statistical Learning Theory
- VC Dimension
- Rademacher Complexity
Statistical Machine Learning - CodeIntermediateNew
Distributional Embeddings for a Multilingual Legal Search
Use a public multilingual corpus (e.g., MultiEURLEX or a subset of EUR-Lex) plus a small hand-built test set of around 100 cross-lingual query-passage pairs. Fine-tune (or evalu…
- Distributional Semantics
- Multilingual NLP
- Sentence Embeddings
Computational Semantics - CodeBeginnerNew
Build Semantic Search for an Internal Engineering Wiki
You receive a Confluence XML export (~12k pages, ~80 MB of text) and a hand-labeled benchmark of 50 internal queries with ground-truth doc IDs. Chunk and embed the corpus with a…
- Embedding Models
- Vector Search
- Pgvector
Vector Databases and Embeddings - AnalysisBeginnerNew
Explain a Credit-Risk Model with SHAP for a Fintech
You receive a trained XGBoost credit-risk model (binary default prediction), the training feature schema (38 features), and a held-out 10,000-sample test set with labels. Comput…
- Shap
- Interpretability
- Fairness Analysis
Explainable and Interpretable AI - AnalysisBeginnerNew
Topic Modeling for Sustainable Fashion Brand's Social Media
You are given a dataset of 50,000 social media posts (text only) mentioning EcoWear. Your task is to preprocess the text, apply an unsupervised topic modeling technique (e.g., L…
- Topic Modeling
- Text Mining
- Lda
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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