Course Outline
Day 1 — Robust Python Foundations & Tooling
Modern Python Features and Typing
- Typing basics, generics, Protocols, and TypeGuard.
- Overview of dataclasses, frozen dataclasses, and attrs.
- Pattern matching (PEP 634+) and idiomatic usage.
Code Quality and Tooling
- Code formatters and linters: black, isort, flake8, ruff.
- Static type checking using MyPy and pyright.
- Pre-commit hooks and developer workflows.
Project Management and Packaging
- Dependency management with Poetry and virtual environments.
- Best practices for package layout, entry points, and versioning.
- Building and publishing packages to PyPI and private registries.
Day 2 — Design Patterns & Architectural Practices
Design Patterns in Python
- Creational patterns: Factory, Builder, Singleton (Pythonic variants).
- Structural patterns: Adapter, Facade, Decorator, Proxy.
- Behavioral patterns: Strategy, Observer, Command.
Architectural Principles
- SOLID principles applied to Python codebases.
- Hexagonal/Clean Architecture and boundaries.
- Dependency injection patterns and configuration management.
Modularity and Reuse
- Designing library vs application code.
- APIs, stable interfaces, and semantic versioning.
- Handling configuration, secrets, and environment-specific settings.
Day 3 — Concurrency, Async IO, and Performance
Concurrency and Parallelism
- Threading fundamentals and GIL implications.
- Multiprocessing and process pools for CPU-bound tasks.
- Guidelines for using concurrent.futures vs multiprocessing.
Async Programming with asyncio
- Async/await patterns, event loop, and cancellation.
- Designing async libraries and interoperability with sync code.
- IO-bound patterns, backpressure, and rate limiting.
Profiling and Optimization
- Profiling tools: cProfile, pyinstrument, perf, memory_profiler.
- Optimizing hot paths and using C-extensions/Numba where appropriate.
- Measuring latency, throughput, and resource utilization.
Day 4 — Testing, CI/CD, Observability, and Deployment
Testing Strategies and Automation
- Unit testing and fixtures with pytest; test organization.
- Property-based testing with Hypothesis and contract testing.
- Mocking, monkeypatching, and testing asynchronous code.
CI/CD, Release, and Monitoring
- Integrating tests and quality gates into GitHub Actions/GitLab CI.
- Building reproducible containers with Docker and multi-stage builds.
- Application observability: structured logging, Prometheus metrics, and tracing.
Security, Hardening, and Best Practices
- Dependency auditing, SBOM basics, and vulnerability scanning.
- Secure coding practices for input validation and secrets management.
- Runtime hardening: resource limits, user rights, and container security.
Capstone Project & Review
- Team lab: design and implement a small service using patterns from the course.
- Testing, type-checking, packaging, and CI pipeline for the project.
- Final review, code critique, and actionable improvement plan.
Summary and Next Steps
Requirements
- Strong intermediate-level Python programming experience.
- Familiarity with object-oriented programming concepts and basic testing.
- Experience using the command line and Git.
Audience
- Senior Python developers.
- Software engineers responsible for Python code quality and architecture.
- Technical leads and MLOps/DevOps engineers working with Python codebases.
Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
