Python Coding Course

Lesson 1 • Core Syntax and Data Types

Introduces variables, literals, and Python's built-in types. Provides the vocabulary needed to write any meaningful expression.

Lesson 2 • Input, Output, and Comments

Covers print(), input(), and inline documentation practices. Enables students to build interactive scripts and write readable code.

Lesson 3 • Running and Debugging Scripts

Explains script execution, error messages, and basic debugging. Students gain confidence fixing syntax and runtime errors independently.

Lesson 4 • Installing Python and Dev Tools

Covers Python installation, version selection, and IDE setup. Establishes the technical foundation every subsequent chapter depends on.

Lesson 1 • For Loops and Iteration

Covers for loops over sequences and the range() function. Students automate repetitive tasks across collections of data.

Lesson 2 • While Loops and Loop Control

Introduces while loops and break, continue, and pass statements. Students handle indefinite repetition and fine-tune loop behaviour.

Lesson 3 • Comprehensions and Generators

Teaches list, dict, and set comprehensions plus generator expressions. Students write concise, memory-efficient iteration patterns.

Lesson 1 • Scope, Namespaces, and Closures

Explains LEGB scope rules, global/nonlocal, and closure mechanics. Students avoid naming conflicts and leverage closures for state retention.

Lesson 2 • Decorators and Function Wrapping

Covers decorator syntax and common use cases like logging and timing. Students extend function behaviour without modifying original source code.

Lesson 3 • Defining and Calling Functions

Covers def syntax, parameters, and return values. Functions become the primary unit of reusable logic throughout the course.

Lesson 4 • Lambda Functions and Higher-Order Functions

Introduces anonymous functions and map(), filter(), and sorted(). Students apply functional patterns to transform and filter data concisely.

Lesson 1 • Dictionaries: Key-Value Storage

Teaches dict creation, access, iteration, and common methods. Dicts are the primary mapping type for structured data in Python.

Lesson 2 • Tuples and Immutability

Explains tuple creation, unpacking, and immutability benefits. Students use tuples for fixed records and safe multi-value returns.

Lesson 3 • Lists: Creation and Manipulation

Covers list creation, indexing, slicing, and mutation methods. Lists serve as the default sequence type in most Python programmes.

Lesson 4 • Sets and Set Operations

Covers set creation, membership testing, and mathematical operations. Students use sets for deduplication and efficient membership checks.

Lesson 1 • Encapsulation and Properties

Explains public, protected, and private naming conventions and @property. Students enforce data integrity through controlled attribute access.

Lesson 2 • Classes, Objects, and Attributes

Introduces class definition, instantiation, and instance attributes. Establishes the object model that all OOP patterns build upon.

Lesson 3 • Methods and Special Methods

Covers instance methods, class methods, static methods, and dunder methods. Students control object behaviour and representation precisely.

Lesson 4 • Polymorphism and Abstract Classes

Covers duck typing, isinstance(), and the abc module. Students write code that works with any object sharing a common interface.

Lesson 1 • Virtual Environments and Dependency Management

Teaches venv, pip, and requirements files for project isolation. Students manage dependencies reproducibly across different machines.

Lesson 2 • Reading and Writing Files

Covers open(), file modes, and the with statement for safe I/O. Students read text and binary files without resource leaks.

Lesson 3 • Exception Handling

Explains try, except, else, finally, and custom exceptions. Students write programmes that degrade gracefully instead of crashing.

Lesson 4 • Modules, Packages, and Imports

Covers import syntax, __init__.py, and the standard library. Students organise code into reusable packages and leverage built-in tools.

Lesson 1 • Exploratory Data Analysis Workflow

Applies cleaning and aggregation skills to a full EDA pipeline. Students produce summary statistics and identify patterns in real data.

Lesson 2 • Grouping, Aggregation, and Merging

Covers groupby(), agg(), merge(), and concat(). Students summarise and combine datasets to answer analytical questions.

Lesson 3 • Pandas Series and DataFrames

Covers Series and DataFrame creation, indexing, and basic inspection. DataFrames become the primary structure for tabular data work.

Lesson 4 • Data Cleaning and Transformation

Teaches handling missing values, type casting, and column operations. Students prepare raw datasets for analysis or modelling.

Lesson 1 • Command-Line Interface Development

Covers argparse and click for building CLI tools. Students expose application functionality through a professional command-line interface.

Lesson 2 • Testing with pytest

Teaches unit testing, fixtures, and test-driven development with pytest. Students verify correctness and prevent regressions automatically.

Lesson 3 • Project Architecture and Design Patterns

Covers MVC separation, single-responsibility principle, and common patterns. Students structure projects so they remain maintainable as they grow.

Lesson 4 • Packaging and Distributing Python Projects

Covers pyproject.toml, build tools, and publishing to a package index. Students share reusable libraries with teammates or the public.