10 must-know algorithms for coding interviews

must know algorithms coding interviews
April 11, 2025
4 min read
By Cojocaru David & ChatGPT

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10 Must-Know Algorithms for Coding Interviews (2024 Guide)

Preparing for coding interviews? Mastering these 10 essential algorithms will give you the confidence and skills to tackle technical challenges at top tech companies. From binary search to dynamic programming, this guide breaks down each algorithm with clear explanations, use cases, and time complexities—helping you optimize your interview performance.

1. Binary Search: Fast Sorted Array Lookup

Binary search efficiently finds a target value in a sorted array by repeatedly halving the search space. It’s a must-know for optimizing search operations.

Why It Matters:

  • Time Complexity: O(log n) – ideal for large datasets.
  • Best For: Sorted arrays, lower/upper bound searches.
  • Example: Finding 5 in [1, 3, 5, 7, 9] takes just 2 steps.

2. Merge Sort: Reliable Divide-and-Conquer Sorting

Merge sort guarantees stable sorting with consistent performance, making it a favorite for large datasets.

Key Features:

  • Stability: Preserves order of equal elements.
  • Time Complexity: O(n log n) – performs well in all cases.
  • Use Cases: External sorting, linked lists.

3. Quick Sort: The Go-To In-Place Sorter

Quick sort’s partitioning strategy makes it one of the fastest general-purpose sorting algorithms.

Optimization Tips:

  • Pivot Choice: Median-of-three reduces worst-case scenarios.
  • Space Efficiency: O(log n) stack space (in-place).
  • Best For: Memory-constrained environments.

4. Breadth-First Search (BFS): Shortest Path Finder

BFS explores graphs level-by-level, perfect for unweighted shortest-path problems.

Practical Applications:

  • Maze solving.
  • Social network friend recommendations.
  • Web crawling (discovering links layer by layer).

5. Depth-First Search (DFS): Deep Graph Exploration

DFS dives deep into graph branches before backtracking—ideal for dependency resolution.

When to Choose DFS:

  • Cycle detection in directed graphs.
  • Topological sorting (e.g., course prerequisites).
  • Solving puzzles with multiple paths.

6. Dijkstra’s Algorithm: Weighted Shortest Paths

This algorithm finds the shortest path in graphs with non-negative edge weights.

Pro Tips:

  • Priority Queue: Use for O((V+E) log V) efficiency.
  • Limitations: Fails with negative weights (use Bellman-Ford instead).
  • Real-World Use: GPS navigation, network routing.

7. Dynamic Programming: Optimize Overlapping Subproblems

DP stores solutions to subproblems to avoid redundant calculations.

Classic DP Problems:

  • Fibonacci sequence (memoization).
  • 0/1 Knapsack (maximizing value with weight constraints).
  • Longest common subsequence (string comparison).

8. Kadane’s Algorithm: Maximum Subarray Sum

Elegantly solves the “maximum subarray” problem in O(n) time.

Why Interviewers Love It:

  • Single Pass: No nested loops needed.
  • Space: O(1) – constant extra space.
  • Applications: Stock profit analysis, signal processing.

9. Union-Find: Network Connectivity Master

Also called Disjoint Set Union (DSU), it manages dynamic connections efficiently.

Key Operations:

  • Union: Merges two sets.
  • Find: Checks set membership.
  • Use Cases: Kruskal’s MST algorithm, social network clusters.

10. Topological Sort: Dependency Ordering

Orders nodes in a Directed Acyclic Graph (DAG) based on dependencies.

Interview Scenarios:

  • Build system dependency resolution.
  • Course scheduling (prerequisites first).
  • Event sequencing in project management.

“The best algorithm is the one you understand deeply—not just the one you memorized.” #CodingInterviews #Algorithms #TechCareers