Week 2 - Structuring Data Pipelines

Introduction to Data Pipelines

Configuration & Secrets (.env)

Separation of Concerns (I/O vs Logic)

Dataclasses for Data Objects

OOP vs Functional Programming

Functional Composition

Testing with Pytest

Practice

Assignment: A Clean Pipeline

Gotchas & Pitfalls

• Teachers

Back to Track

5. OOP vs Functional Programming

Content coming soon...

Concepts to Cover

• The core difference: logic bound to data (OOP) vs separate from data (FP)
• State: mutable object properties vs immutable data transformations
• When to use OOP: Dataclasses for data structure, avoid "God Classes"
• When to use FP: pure functions for transformation logic
• The hybrid approach: Dataclasses for shape, functions for processing
• Testing implications: mocking object state vs simple input/output verification
• Hands-on comparison: user.save() (OOP) vs save_user(user) (FP)

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