Lesson 059
Functional Programming
Pure Functions · Immutability · Map / Filter / Reduce
1:00How to write programs as pipelines of pure functions over immutable data — and why purity, higher-order functions, and map/filter/reduce make code safer, testable, and naturally concurrent.
By the end, you can
- Explain the difference between imperative and functional (declarative) programming styles.
- State the two defining properties of a pure function and identify pure vs impure examples.
- List at least four examples of side effects.
- Explain referential transparency and why it enables safe memoization.
- Explain what immutability means and why it eliminates race conditions.
- Distinguish first-class functions from higher-order functions.
- Apply map, filter, and reduce by hand to compute results on small lists.
- Explain the reduce edge case for an empty list with no initial value.
- Describe what a closure is and trace its captured variable through a call.
- Arrange filter → map → reduce into a correct data pipeline.
- Explain why pure + immutable code is naturally concurrent and how MapReduce exploits it.
- Recognise functional programming in everyday tools (spreadsheets, React, Redux).
- Refute the myths that FP is "just lambdas" or "purely academic," pointing to its practical payoff — easier testing, safe concurrency, and predictable code — and its adoption in mainstream languages and data pipelines.
Up next in Recursion, Paradigms & Algorithm Analysis
