Introduction

Here are a few things to consider when trying to identify what data structure to use for algorithms.

We'll cover the following sorting algorithms.

Introduction
Array/List
Hash Table/Map
Set
Stack
Queue
Deque
Linked List
Tree
Binary Tree
Binary Search Tree(BST)
Heap
Segment Tree/Fenwick Tree
Trie(Prefix Tree)
Graph
Union-Find(Disjoint Set)
Conclusion

Array/List

Hash Table/Map

Set

Stack

Last In First Out(L.I.F.O.)

Situation	Use	Reasoning / Pattern
Need to process elements in reverse order	Stack	LIFO (Last In, First Out)
Tracking function calls / recursion	Stack	Call stack mirrors nested calls
Undo functionality (e.g., editor)	Stack	Last action undone first
Balanced parentheses / bracket matching	Stack	Pairs resolved in reverse order

Queue

First In First Out(F.I.F.O.)

Situation	Use	Reasoning / Pattern
Need to process elements in the order received	Queue	FIFO (First In, First Out)
Level-order traversal of tree (BFS)	Queue	Process nodes layer by layer
Scheduling tasks / simulation	Queue	Tasks processed in arrival order

Deque

Double ended queue

Linked List

A linear data structure where elements, called nodes, are linked together.

Tree

A data structure composed of nodes that contain values & leafs.

Binary Tree

A tree where each node has only two children nodes, left & right.

Binary Search Tree(BST)

A binary search tree where the value of the left child is less than the parent node's value. The right node's value is greater than the parent node's value.

Heap

Situation	Use
Always want largest/smallest	Max/Min heap
Keep top K of something	Min/Max heap of size K
Custom processing order	Priority queue (heap)
Streaming data	Heap(s) to track top items

Segment Tree/Fenwick Tree

Trie(Prefix Tree)

Graph

Nodes & Edges. Node's are airports and edges are the flights between them.

Furthermore graphs can be directed or undirected meaning that the flights can be listed as pointing from one airport to another or both ways(undirected).

The edges can have weighted as well meaning that the edges from one node to another may vary in value. For example the price from an airport to airport a & b may differ.

Union-Find(Disjoint Set)

Conclusion

Data Structure	Typical Use Cases
Array/List	Index-based access, iteration
Hash Table (Map)	Fast lookups, counting, caching
Set	Fast membership tests, removing duplicates
Stack	Undo operations, expression parsing, backtracking, DFS
Queue	Scheduling, BFS, order of processing
Deque	Sliding window problems, double-ended queue operations
Linked List	Efficient insertions/deletions at head/tail
Tree	Representing data with a parent-child relationship, such as file systems, organizational charts, or XML/HTML documents.
Binary Tree	Hierarchical data, expression parsing
Binary Search Tree (BST)	Sorted data, efficient range queries
Heap (Priority Queue)	Getting largest/smallest elements efficiently, scheduling
Trie (Prefix Tree)	Prefix search, autocomplete
Graph	Network problems, path-finding, connected components
Union-Find (Disjoint Set)	Connectivity queries, Kruskal’s algorithm
Segment Tree / Fenwick Tree	Range queries and updates (e.g., sum, min, max)