Python Programming
If you like applying Python Programming, every challenge here gives you a chance to practice it on a real industry brief.
CodeBeginnerNewBuild a Search-Based Delivery Router for a Madrid Grocery Startup
Build a Python service that exposes a single /route endpoint. It accepts a courier's starting location, a list of delivery stops, and a traffic window, and returns the stops in …
- Search Algorithms
- Intelligent Agents
- Python Programming
Open coursework- CodeBeginnerNew
Build a Functional Image-Filter Pipeline for a Photography SaaS
Design a Filter as a function from (pixel matrix → pixel matrix). Build a compose helper that takes a list of filters and returns a single composed filter (function composition)…
- Higher Order Functions
- Function Composition
- Abstraction
Open coursework 
CodeBeginnerNewKnowledge-Graph Recommender for a Lisbon Bookstore
Using the catalog sample (catalog_sample), build a knowledge graph whose nodes are books, authors, genres, themes, eras, and awards, and whose edges capture relationships such a…
- Knowledge Representation
- Knowledge Graphs
- Python Programming
Open coursework- CodeBeginnerNew
Refactor a Legacy Reporting Script for a Renewable-Energy Startup
Read the existing script and identify 5-7 distinct responsibilities tangled together. Refactor into 3 layers: data loaders (CSV, REST API), transformers (pure functions over row…
- Abstraction
- Higher Order Functions
- Refactoring
Programming Abstractions 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
CodeBeginnerNewBuild a Pricing-Aware Scheduling Agent for Smart Thermostats
Using only the materials provided, build an intelligent agent with four working parts: perception (read the sensor history), learning (cluster each household's comfort intervals…
- Intelligent Agents
- Basic Learning
- Python Programming
Open coursework- CodeBeginnerNew
Churn-Prediction Model for a B2B Vertical SaaS
Use 18 months of anonymized data (provided) covering: usage events, login frequency, support tickets, NPS responses, billing health, feature adoption, practice firmographics. De…
- Supervised Learning
- Python Programming
- Ml Applications
Machine Learning (CS Elective) - StrategyBeginnerNew
Toil Audit + Automation Sprint for a Platform Team
Week 1-2: every team member logs every toil instance for 10 working days (timestamp, category, duration). Categorize using the Google SRE toil taxonomy (manual, repetitive, auto…
- Toil Reduction
- Automation
- Reliability Engineering
Open coursework - CodeBeginnerNew
Characterization Tests for an Untested Pricing Engine
Drive the pricing engine with around 800 representative input vectors generated from anonymized production quotes (provided). For each vector, capture the current output as the …
- Characterization Tests
- Approval Testing
- Legacy Code
Refactoring and Code Smell Detection - 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.
- AnalysisBeginnerNew
Customer-Segmentation Study for a DTC Subscription Box
Use 18 months of anonymized data: order history, churn events, NPS responses, box-rating data, referral activity, marketing-channel attribution. Engineer features (RFM-style + b…
- Unsupervised Learning
- Python Programming
- Ml Applications
Machine Learning (CS Elective) - CodeBeginnerNew
Build a Safe Expression Evaluator for a Math-Learning App
Working only from the materials provided, build a self-contained expression evaluator in Python (TypeScript is acceptable if you mirror the same structure) that never calls eval…
- Recursion
- Higher Order Functions
- Parsing
Open coursework - CodeBeginnerNew
Build an Embedding-Based Semantic Search for a Legal-Document Corpus
Embed the 380k-document corpus using a multilingual sentence-transformer (e.g. multilingual MPNet or LaBSE). Store embeddings in FAISS or pgvector. Build a search service that r…
- Deep Learning
- Ml Applications
- Python Programming
Machine Learning (CS Elective)
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.