Learn Functional Programming in Python Course Syllabus

Overview: This hands-on course guides beginners through the core concepts of functional programming in Python, blending theory with practical exercises to build clean, efficient, and reusable code. Over approximately 16.5 hours, learners progress from foundational principles to real-world applications, mastering key tools like higher-order functions, lambda expressions, decorators, and recursion. Each module reinforces learning with coding activities, ensuring a deep understanding of functional patterns and their benefits in Python development.

Module 1: Introduction to Functional Programming

Estimated time: 1.5 hours

• What is functional programming
• Imperative vs. functional style
• Core benefits of functional programming
• Comparing imperative and functional code examples in Python

Module 2: Functions as First-Class Objects

Estimated time: 2 hours

• Assigning functions to variables
• Passing functions as arguments
• Returning functions from other functions
• Building higher-order functions and reusable utilities

Module 3: Pure Functions and Immutability

Estimated time: 2 hours

• Understanding side effects
• Referential transparency
• Writing pure functions
• Using tuples and frozensets for immutability
• Refactoring code to eliminate side effects

Module 4: Built-In Functional Tools

Estimated time: 2.5 hours

• Using map() for transformations
• Filtering data with filter()
• Aggregating with reduce()
• Leveraging zip() and enumerate()
• Performing data transformations with functional constructs

Module 5: Lambda Expressions and Closures

Estimated time: 2 hours

• Creating anonymous functions with lambda
• Variable scoping in closures
• Capturing variables in nested functions
• Building compact operations using lambdas and closures

Module 6: Recursion and Tail Calls

Estimated time: 2 hours

• Recursion patterns in Python
• Implementing factorial and Fibonacci using recursion
• Avoiding stack overflow issues
• Converting recursive logic to iteration

Module 7: Decorators and Composition

Estimated time: 2.5 hours

• Writing custom decorators
• Chaining functions and decorators
• Function composition and pipelines
• Creating decorators for logging, timing, and validation

Module 8: Real-World Applications

Estimated time: 2 hours

• Applying functional programming in data processing
• Using functional patterns in event-driven design
• Building a small project with functional techniques

Prerequisites

• Basic knowledge of Python syntax
• Familiarity with functions and data types
• Understanding of control flow statements

What You'll Be Able to Do After

• Explain functional programming principles and their advantages
• Use first-class and higher-order functions effectively
• Apply map, filter, reduce, and list comprehensions to data tasks
• Write pure functions and manage immutability
• Build and use decorators, closures, and recursive functions