After this lesson, you will be able to: Understand why the choice of data structure determines performance, and build a mental model for evaluating structures by their operation costs.
Before learning any specific structure, you need to understand why it matters which one you pick. This lesson shows the same task solved two ways, one fast and one slow, purely because of the data structure choice. That gap is the whole point of this sub-track.
This is a free introductory lesson. No purchase required.
Say you have a list of one million users and you need to check 'does this email already exist?' thousands of times. If your users are in a plain array, each check scans the whole array: up to a million comparisons, every time. If your users are in a hash set, each check is roughly one step regardless of size. Same task, same data, but one version finishes instantly and the other crawls. The only difference is the structure you stored the data in.
Two ways to answer the same question. One is O(n) per lookup, one is O(1).
# Slow: scans the whole list every time (O(n) per lookup)def exists_slow(target):for e in emails:if e == target:return Truereturn False# Fast: build a set once, then each lookup is ~O(1)email_set = set(emails)def exists_fast(target):return target in email_set# For 10,000 lookups over 1M emails:# exists_slow -> up to 10 billion comparisons# exists_fast -> ~10,000 hash lookups
For each data structure in this sub-track, ask the same five questions. How fast is access (reading element i)? How fast is search (finding a value)? How fast is insertion? How fast is deletion? How much memory does it use? Once you can answer those for arrays, hash tables, trees, and the rest, choosing the right one becomes mechanical instead of a guess.
This sub-track covers arrays and dynamic arrays, linked lists, stacks, queues, hash tables, trees and binary search trees, heaps, graphs, and tries. For each one you will learn how it works in memory, what its operations cost, where it shines, and where it is the wrong choice. By the end you will implement a small library of these structures from scratch.