Code Practice
Learning Roadmap
Click any course to start learning
This module provides hands-on programming exercises for Operations Research, Data Science, and AI, helping you turn theory into code.
In-Browser Execution
All code exercises run directly in your browser — no installation needed. Powered by Pyodide (WebAssembly Python), the Python environment loads on first run (~12MB).
Operations Research
Foundations
| Course | Exercises | Start |
|---|---|---|
| Operations Research | 12 lessons | Lesson 1 → |
| Linear Programming | 12 lessons | Lesson 1 → |
| Integer Programming | 12 lessons | Lesson 1 → |
| Convex Optimization | 12 lessons | Lesson 1 → |
Advanced Topics
| Course | Exercises | Start |
|---|---|---|
| Nonlinear Optimization | 12 lessons | Lesson 1 → |
| Numerical Optimization | 12 lessons | Lesson 1 → |
| Stochastic Programming | 12 lessons | Lesson 1 → |
| Robust Optimization | 12 lessons | Lesson 1 → |
| Multi-Objective Optimization | 12 lessons | Lesson 1 → |
| Optimal Control | 12 lessons | Lesson 1 → |
Combinatorics & Networks
| Course | Exercises | Start |
|---|---|---|
| Combinatorial Optimization | 12 lessons | Lesson 1 → |
| Dynamic Programming | 12 lessons | Lesson 1 → |
| Network Optimization | 12 lessons | Lesson 1 → |
| Metaheuristics | 12 lessons | Lesson 1 → |
| Game Theory | 12 lessons | Lesson 1 → |
| Queueing Theory | 12 lessons | Lesson 1 → |
| Simulation Methods | 12 lessons | Lesson 1 → |
Data Science
Foundations
| Course | Exercises | Start |
|---|---|---|
| Probability & Statistics | 12 lessons | Lesson 1 → |
| Statistical Learning | 12 lessons | Lesson 1 → |
| Machine Learning | 12 lessons | Lesson 1 → |
Advanced
| Course | Exercises | Start |
|---|---|---|
| Bayesian Methods | 12 lessons | Lesson 1 → |
| Data Mining | 12 lessons | Lesson 1 → |
| Time Series Analysis | 12 lessons | Lesson 1 → |
| Causal Inference | 12 lessons | Lesson 1 → |
Artificial Intelligence
Core
| Course | Exercises | Start |
|---|---|---|
| Deep Learning | 12 lessons | Lesson 1 → |
| Reinforcement Learning | 12 lessons | Lesson 1 → |
| Large Language Models | 12 lessons | Lesson 1 → |
Applications
| Course | Exercises | Start |
|---|---|---|
| Computer Vision | 12 lessons | Lesson 1 → |
| Natural Language Processing | 12 lessons | Lesson 1 → |
| Graph Neural Networks | 12 lessons | Lesson 1 → |
| Generative Models | 12 lessons | Lesson 1 → |
Industry Cases
5 complete industry-grade optimization cases covering MILP, QP, network flow, and multi-objective optimization. Each case includes real-world context, full mathematical modeling, code templates (with TODOs), and reference solutions.
| # | Case | Type | Difficulty | Start |
|---|---|---|---|---|
| 1 | Facility Location | MILP | ★★ | Start → |
| 2 | Portfolio Optimization | QP | ★★ | Start → |
| 3 | Supply Chain Network | LP/Flow | ★ | Start → |
| 4 | Workforce Scheduling | MILP | ★★★ | Start → |
| 5 | Multi-Objective Production | MO-MILP | ★★★ | Start → |
Premium Content
The Code Practice module requires a subscription. Course overview pages are free.