Engineering with Python

Topics covered

• Variables
• Types
• Conditionals
• Loops
• Functions
• Modules
• Packages
• Exceptions
• Files
• OOP
• Classes
• Inheritance
• Decorators
• Generators
• Iterators
• Context managers
• Typing
• Async
• Concurrency
• Testing
• Profiling

Lessons in this unit

1. Variables, types and expressions 60 min
   The building blocks of every Python program — names, values, types and the basic operations between them.
2. Operators, formatting and precision 75 min
   The finer details of numbers in Python — the order in which things are calculated, floor division and remainder, how to show results with just the right format and why floats aren't exact.
3. Conditionals and loops 70 min
   How a program decides what to do and repeats work — if, for, while, break and continue.
4. Functions 80 min
   Packaging logic with a name, inputs and an output — and treating functions as values.
5. Lists and tuples 95 min
   Storing many values in order and working with them — the structure you'll handle data with every day.
6. Dictionaries and sets 80 min
   Looking things up by name, counting and accumulating with dictionaries, and removing duplicates or comparing collections with sets.
7. Comprehensions and the collections module 80 min
   Transforming and filtering collections in one readable line, and counting and grouping with Counter and defaultdict.
8. Exceptions and error handling 80 min
   When something goes wrong, fail clearly or recover on purpose — never silently.
9. Files, CSV and JSON 75 min
   Reading and writing the formats where the datasets, configurations and results of any AI project live.
10. Classes and objects 65 min
    Bringing data and the code that handles it together in a single object with state — the shape of a model, a tokeniser or an optimiser.
11. Inheritance, composition and special methods 90 min
    Reuse and specialise classes with inheritance and super(), combine objects with composition and make your classes behave like built-in types.
12. Iterators and generators 90 min
    Producing data on demand, one item at a time, to work with datasets that don't fit in memory.
13. Decorators and context managers 90 min
    Adding behaviour to functions without touching them (timing, caching, retrying) and guaranteeing resource cleanup with with.
14. Types, dataclasses and tests 75 min
    Document contracts with type annotations, model data with dataclasses and prove that your code works with automated tests.
15. Advanced Python (optional): measure before you optimise 90 min · optional
    Timing code properly, finding where the time really goes with a profiler and checking how the cost grows as the data grows.
16. Advanced Python (optional): concurrency and async 100 min · optional
    Doing several things at once with threads, processes and asyncio, and knowing which to choose depending on whether your program computes or waits.

Prerequisites

Before this unit it helps to have done:

• Digital foundations (Course 1 · Unit 1)