Beginner Python Course

Lesson 1 • Using the Interactive Shell

Execute Python expressions directly in the REPL for instant feedback. This skill accelerates experimentation in all later chapters.

Lesson 2 • Choosing and Configuring an Editor

Compare code editors and IDEs suited for beginners. Configuring a proper editor reduces friction throughout the course.

Lesson 3 • Installing Python on Your Machine

Download and install Python on Windows, macOS, and Linux. Establishes the runtime foundation every subsequent chapter depends on.

Lesson 1 • Comparison and Logical Operators

Produce Boolean results using comparison and logical operators. These operators drive every conditional and loop introduced in the next chapter.

Lesson 2 • Core Numeric and Text Types

Work with integers, floats, and strings as Python's primary data types. Understanding types prevents common bugs in arithmetic and text handling.

Lesson 3 • Variables and Assignment

Declare variables, assign values, and follow Python naming rules. Variables are the building blocks for every program written in this course.

Lesson 4 • Arithmetic and String Operators

Apply arithmetic operators and string concatenation to produce new values. Operator knowledge is required for all logic and data-processing tasks ahead.

Lesson 1 • for Loops and the range() Function

Iterate over sequences and numeric ranges using for loops. for loops are the primary tool for processing collections introduced in the next chapter.

Lesson 2 • while Loops and Loop Control

Repeat code blocks while a condition holds true and control loop execution. while loops handle indefinite repetition common in user-input scenarios.

Lesson 3 • if, elif, and else Statements

Branch program logic based on Boolean conditions using if, elif, and else. Conditional logic is the foundation of all decision-making in Python programs.

Lesson 1 • Tuples and Immutability

Define tuples as fixed sequences and understand when immutability is preferable. Tuples protect data integrity and are used in function returns and unpacking.

Lesson 2 • Modifying Lists

Add, remove, and reorder list elements using built-in methods. Mutable lists enable dynamic data management throughout the course projects.

Lesson 3 • Sets and Membership Testing

Use sets for unique-value storage and fast membership checks. Sets simplify deduplication and intersection tasks common in data processing.

Lesson 4 • Lists: Creation and Indexing

Create lists, access elements by index, and slice sublists. Lists are Python's most versatile sequence type and appear in virtually every program.

Lesson 1 • Lambda Functions and Built-ins

Write concise anonymous functions with lambda and leverage built-in functions. These tools reduce boilerplate in sorting, filtering, and mapping tasks.

Lesson 2 • Defining and Calling Functions

Use the def keyword to create named functions and call them with arguments. Functions are the primary unit of code reuse in every Python project.

Lesson 3 • Parameters, Defaults, and Keyword Args

Design flexible functions using default parameter values and keyword arguments. Flexible signatures make functions adaptable without breaking existing calls.

Lesson 4 • Variable Scope and Namespaces

Distinguish local and global scope to avoid naming conflicts. Understanding scope prevents subtle bugs when functions share variable names.

Lesson 1 • Reading and Writing CSV Files

Parse and produce CSV data using the csv module for tabular file handling. CSV is the most common format for exchanging structured data in professional settings.

Lesson 2 • Raising and Creating Exceptions

Signal errors intentionally with raise and define custom exception classes. Custom exceptions make library and application code self-documenting and debuggable.

Lesson 3 • Working with File Paths

Navigate directories and construct file paths using the os and pathlib modules. Robust path handling prevents file-not-found errors across operating systems.

Lesson 4 • Reading and Writing Text Files

Open files with open(), read content, and write new data using context managers. File I/O is essential for any program that stores or loads persistent data.

Lesson 1 • Creating Your Own Modules

Split code across multiple .py files and import them as custom modules. Modular design keeps projects maintainable as they grow in complexity.

Lesson 2 • Importing Modules

Use import, from-import, and aliases to access module functionality. Proper import style is a prerequisite for working with any library in Python.

Lesson 3 • Virtual Environments for Projects

Isolate project dependencies using virtual environments to prevent version conflicts. Every professional Python project should use a dedicated virtual environment.

Lesson 4 • Exploring the Standard Library

Apply frequently used standard library modules to solve common programming tasks. The standard library reduces development time and improves code reliability.

Lesson 1 • Inheritance and Code Reuse

Create subclasses that inherit and extend parent class behaviour. Inheritance eliminates duplication when modelling hierarchical relationships between entities.

Lesson 2 • The __init__ Method and Attributes

Initialise object state with __init__ and store data as instance attributes. Proper initialisation ensures objects are valid and ready to use immediately.

Lesson 3 • Methods and Encapsulation

Define instance methods to encapsulate behaviour and protect internal state. Encapsulation reduces coupling and makes classes easier to test and maintain.

Lesson 4 • Special Methods and Operator Overloading

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