The Most Common Data Structures and Algorithms

A Friendly Guide

Published on 07 Aug 2025
data structures algorithms

Common Data Structures

Data structures are ways of organizing and storing data so computers can use it efficiently. Here are some of the most widely used ones:

Arrays

  • Fixed-size collections of elements

  • Fast access using indexes

  • Great for sequential data (e.g., lists of numbers)

Linked Lists

  • Nodes connected by pointers

  • Easy insertions and deletions

  • Useful for queues, stacks, and dynamic memory

Stacks

  • “Last In, First Out” (LIFO)

  • Used in undo operations, recursion, and compilers

Queues

  • “First In, First Out” (FIFO)

  • Used in scheduling, messaging systems, and buffering

Hash Tables / Hash Maps

  • Key–value storage with near-constant lookup time

  • Used in dictionaries, caches, and databases

Trees

  • Hierarchical data structure

  • Common examples:

    • Binary Trees

    • Binary Search Trees (BST)

    • Heaps

  • Used in filesystems, search, and priority queues

Graphs

  • Nodes (vertices) connected by edges

  • Models relationships and networks

  • Used for maps, social networks, routing systems

Common Algorithms

Algorithms are step-by-step methods for solving problems. These classics show up everywhere:

Sorting Algorithms

  • Bubble Sort (simple, but slow)

  • Merge Sort (fast, divide-and-conquer)

  • Quick Sort (fast in practice)

  • Heap Sort (uses a heap structure)

Searching Algorithms

  • Linear Search (scan one by one)

  • Binary Search (fast search in sorted data)

Graph Algorithms

  • Breadth-First Search (BFS)

  • Depth-First Search (DFS)

  • Dijkstra’s Algorithm (shortest path)

  • A* Search (shortest path with heuristics)

Dynamic Programming

  • Breaks problems into overlapping subproblems

  • Examples: Fibonacci, knapsack, path optimization

Greedy Algorithms

  • Always choose the locally best option

  • Examples: scheduling, coin change (sometimes)

Why Are They So Important?

Data structures and algorithms matter because they:

  • Make software faster and more efficient

  • Reduce memory usage

  • Enable solutions to complex, real-world problems

  • Form the foundation of:

    • System design

    • Machine learning

    • Databases

    • Operating systems

They also teach you how to think like a problem solver, not just how to code.

Don’t Get Overwhelmed

Learning DS&A can feel intimidating — and that’s completely normal.

A few tips:

  • Start small and build gradually

  • Focus on concepts, not memorisation

  • Practise with real problems

  • Revisit topics — mastery comes through repetition

You don’t need to know everything at once. Learn as you go.

Summary

Data structures help organise data. Algorithms help process it. Together, they power nearly everything computers do.

  • Learn the fundamentals

  • Practice steadily

  • Understand why they matter

With time and patience, these concepts will become some of the most valuable tools in your developer toolkit.