Course Outline
Day 1 — Robust Python Foundations & Tooling
Modern Python Features and Typing
- Basics of typing, 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 its boundaries
- Dependency injection patterns and configuration management
Modularity and Reuse
- Distinguishing between library and application code design
- APIs, stable interfaces, and semantic versioning
- Managing configuration, secrets, and environment-specific settings
Day 3 — Concurrency, Async IO, and Performance
Concurrency and Parallelism
- Threading fundamentals and the implications of the GIL
- Multiprocessing and process pools for CPU-bound tasks
- Choosing between concurrent.futures and multiprocessing
Async Programming with asyncio
- Async/await patterns, the event loop, and cancellation mechanisms
- Designing async libraries and interoperability with synchronous code
- Handling IO-bound patterns, backpressure, and rate limiting
Profiling and Optimization
- Profiling tools: cProfile, pyinstrument, perf, memory_profiler
- Optimizing hot paths and utilizing 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 learned in 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 and fundamental testing concepts
- 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
