Advanced Python Programming Course in Africa

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• By the end of this advanced Python programming course, the learner will be able to:
• Explain Python’s object model, attribute lookup, method resolution and core data protocols.
• Design expressive classes with special methods, descriptors and controlled object creation.
• Build reusable decorators, context managers and lazy data-processing pipelines.
• Apply advanced type annotations, protocols and generics to clarify program contracts.
• Select and implement appropriate concurrency using threads, processes or `asyncio`.
• Profile execution time and memory use and optimise code from measured evidence.
• Design stable package interfaces with robust testing, logging and compatibility practices.
• Deliver and defend a production-quality Python library built with the standard library.

Modules

1. 1

   Python’s data model

   Objects, identity, type, value and mutability.Attribute lookup, namespaces and method resolution order.Special methods for representation, comparison, hashing, containers and callability.Designing classes that participate naturally in Python protocols.The relationship between syntax and underlying data-model methods.
2. 2

   Advanced class design

   Composition, mixins and cooperative multiple inheritance.`super()` and method resolution in complex hierarchies.Properties and the descriptor protocol.Class creation hooks, `__init_subclass__` and metaclasses at an appropriate depth.Memory-aware objects with `__slots__`.Choosing explicit, maintainable designs over unnecessary metaprogramming.
3. 3

   Decorators and context management

   Closures, free variables and function metadata.Function decorators with arguments.Class decorators and decorator classes.Synchronous and asynchronous context managers.Reusable resource, timing, validation and tracing patterns.
4. 4

   Advanced iteration and lazy pipelines

   Iterator protocol design and delegation with `yield from`.Generator communication, return values and controlled termination.Combining generator stages into lazy pipelines.Useful patterns from `itertools` and `functools`.Balancing readability, memory efficiency and error handling.
5. 5

   Advanced typing and interface contracts

   Deep use of annotations for functions, classes and collections.Type aliases, unions, literals, callables and overloaded interfaces.Generic functions and generic classes.Structural subtyping with protocols.Typed dictionaries and data records.Runtime limits of annotations and responsible use of static analysis.
6. 6

   Concurrency and parallel work

   Concurrency, parallelism, I/O-bound work and CPU-bound work.Thread lifecycle, locks, queues and safe shared state.Process pools, inter-process boundaries and serialisation constraints.Coroutines, tasks, task groups, cancellation and timeouts with `asyncio`.Selecting the simplest concurrency model that fits a measured problem.
7. 7

   Performance, memory and interpreter behaviour

   Establishing baselines with `timeit` and `cProfile`.Finding hot paths and interpreting profiler output.Tracking allocations with `tracemalloc`.Reference counting, cyclic garbage collection and object lifetime.Algorithmic improvements, caching and data-structure choices.Avoiding premature optimisation and documenting performance decisions.
8. 8

   Package architecture, quality and final project

   Designing small, stable public interfaces.Dependency direction, separation of concerns and extension points.Advanced testing with mocks, subtests and concurrency-aware cases.Logging, error boundaries and graceful shutdown.Compatibility, deprecation and documentation practices.Final project: a documented, tested and profiled Python library with synchronous and concurrent workflows.