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ArraysLinked ListsHashmapsQueuesTreesHeapsGraphsMatricesTriesUnion-FindSegment Trees

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IntroductionData Structures

Data Structures

Special storage formats for organizing and managing data efficiently.

Definition

Data structures are specialized storage formats for organizing, processing, retrieving, and storing data. They allow for efficient access and modification of data, making it possible to run complex computations and tasks. The right data structure can make the difference between code that runs in milliseconds and code that takes minutes.

Learning Path

Follow this path from foundational structures to advanced ones. Each topic builds on concepts from the previous, so working through them in order gives you the strongest understanding.

Foundational

Arrays

→

Contiguous blocks of memory storing elements of the same type, accessed by index.

3
7
1
9
2
5

Linked Lists

→

Nodes connected by pointers, enabling efficient insertion and deletion anywhere.

A
→
B
→
C
→
D
→ null

Hashmaps

→

Key-value stores with near-constant time lookups using hash functions.

"name": "Ada""age": 36"lang": "py"

Queues

→

First-in-first-out structures for ordered processing and scheduling.

IN →
1
2
3
4
5
→ OUT
Intermediate

Trees

→

Hierarchical structures with parent-child relationships, rooted at a single node.

Heaps

→

Complete binary trees maintaining a min or max ordering property for priority access.

13579811

Graphs

→

Vertices connected by edges, modeling relationships and networks of any shape.

Matrices

→

Two-dimensional arrays organized in rows and columns for grid-based problems.

1
0
1
0
1
0
1
0
1
Advanced

Tries

→

Prefix trees for fast string lookups, autocomplete, and dictionary operations.

*cdaooa

Union-Find

→

Disjoint set structure for tracking connected components and merging groups.

Segment Trees

→

Tree structures for efficient range queries and updates on intervals of data.

361026371115