Starting Python Programming Course

Lesson 1 • Installing Python and Your Editor

Install Python and a code editor to create a ready-to-use development environment. This foundation enables all hands-on work throughout the course.

Lesson 2 • Writing and Running Script Files

Create .py files and run them from the terminal to produce repeatable programs. Scripts form the basis of every project built in later chapters.

Lesson 3 • Using the Interactive Interpreter

Execute Python expressions live in the REPL to observe immediate results. This skill accelerates experimentation and debugging throughout the course.

Lesson 1 • Core Numeric and Text Types

Work with integers, floats, and strings as Python's fundamental data types. These types appear in virtually every real-world program.

Lesson 2 • Type Conversion and Input

Convert between types and capture user input to make programs interactive. Input handling connects raw data to typed variables.

Lesson 3 • Assigning and Naming Variables

Assign values to named variables and follow Python naming conventions. Variables are the primary mechanism for storing program state.

Lesson 4 • Arithmetic and String Operators

Apply arithmetic operators to numbers and concatenation to strings to compute results. Operator precedence rules determine evaluation order.

Lesson 1 • Repeating with while Loops

Execute a block repeatedly while a condition holds true using while loops. This pattern handles unknown iteration counts and event-driven repetition.

Lesson 2 • Loop Control Statements

Alter loop execution with break, continue, and else to implement precise iteration logic. These controls add flexibility beyond simple start-to-finish loops.

Lesson 3 • Iterating with for Loops

Iterate over sequences using for loops to process each element systematically. The for loop is the primary tool for working with collections.

Lesson 4 • Combining Conditions and Loops

Integrate conditionals inside loops to filter, accumulate, and transform data. This combination underpins most real-world data-processing algorithms.

Lesson 1 • Keyword and Variable Arguments

Use keyword arguments and *args/**kwargs to build flexible function interfaces. These patterns appear throughout Python's standard library and third-party packages.

Lesson 2 • Parameters, Arguments, and Return Values

Pass data into functions via parameters and retrieve results with return. This mechanism enables functions to generalize across different inputs.

Lesson 3 • Defining and Calling Functions

Create named functions with def and invoke them to execute reusable logic. Functions are the primary unit of code organisation in Python.

Lesson 4 • Lambda Functions and Built-ins

Write concise anonymous functions with lambda and leverage built-in functions for common tasks. These tools reduce boilerplate in data transformation code.

Lesson 1 • Tuples and Sets

Use immutable tuples for fixed data and sets for unique, unordered collections. Each type enforces constraints that prevent accidental data mutation.

Lesson 2 • Lists: Creation and Manipulation

Build ordered, mutable sequences with lists and apply methods to modify them. Lists are the most versatile collection type in Python.

Lesson 3 • List Comprehensions

Generate new lists concisely using comprehension syntax with optional filtering. Comprehensions replace verbose loops and improve code readability.

Lesson 4 • Dictionaries: Key-Value Storage

Map keys to values with dictionaries to enable fast lookups and structured records. Dictionaries model real-world entities like user profiles and configuration data.

Lesson 1 • Creating Your Own Modules

Organise related functions into custom modules and import them across projects. Custom modules are the first step towards building maintainable, multi-file programs.

Lesson 2 • Reading and Writing Text Files

Open files with the with statement and perform read/write operations safely. File I/O connects programs to persistent storage on disk.

Lesson 3 • Handling File Errors Gracefully

Catch file-related exceptions to prevent crashes when files are missing or unreadable. Robust error handling is essential for production-quality scripts.

Lesson 4 • Working with CSV and JSON Data

Parse and produce CSV and JSON files using the csv and json standard modules. These formats are the most common data exchange formats in professional environments.

Lesson 1 • Special Methods and Operator Overloading

Implement dunder methods to integrate custom classes with Python's built-in syntax. Special methods make objects behave like native Python types.

Lesson 2 • Inheritance and Method Overriding

Create subclasses that inherit and extend parent class behaviour to promote code reuse. Inheritance models hierarchical relationships between related entities.

Lesson 3 • Classes and Objects

Define classes with attributes and methods, then instantiate objects from them. Classes bundle data and behaviour into a single, reusable unit.

Lesson 4 • Encapsulation and Properties

Control attribute access using naming conventions and property decorators to protect internal state. Encapsulation reduces unintended side effects in complex programs.

Lesson 1 • try, except, else, and finally

Structure exception-handling blocks to catch specific errors and guarantee cleanup code runs. Precise exception handling prevents silent failures and resource leaks.

Lesson 2 • Introduction to Unit Testing

Write automated tests with the unittest module to verify function correctness. Tests catch regressions early and document expected behaviour for future developers.

Lesson 3 • Raising and Creating Custom Exceptions

Raise exceptions intentionally and define custom exception classes to signal domain-specific errors. Custom exceptions make library and application code self-documenting.

Lesson 4 • Debugging Techniques and Tools

Use print-based debugging, the built-in debugger, and logging to locate and fix defects. Systematic debugging reduces time spent diagnosing production issues.