Algorithms & Data Structures

Algorithms & Data Structures

The data structure or algorithm you pick decides whether your application still works when the dataset grows from a hundred rows to a million. Big O notation (O(1), O(n), O(log n), O(n log n)) gives you a practical vocabulary for time and space complexity, so you can reason about arrays, maps/dictionaries, and trees, and choose between, say, a linear scan and a binary search, or a simple sort and a more efficient one.

You don't need academic proofs at this level — you need to recognise which structure and which approach fits a given data size, access pattern, and performance requirement, and to be able to justify that choice.

Starting Points

• Programming foundations (knowledge)
• Head First Algorithms and Data Structures. O'Reilly.

Key Points

• You explain Big O notation at a basic level to describe the time and space complexity of common operations.
• You select appropriate data structures (arrays, lists, maps, trees) based on use case, performance needs, and access pattern.
• You implement and use basic sorting and searching algorithms (e.g. linear vs. binary search, bubble/selection/insertion sort) in practical code.
• You weigh the trade-offs between different data structures and algorithms for a given problem.