Must-Know Algorithms for Every Coder 🚀

Understanding key algorithms is essential for writing efficient code, passing technical interviews, and solving real-world problems. Here are some of the most useful algorithms every programmer should know:

1️⃣ Sorting Algorithms

Sorting is fundamental for organizing data efficiently.

✔ QuickSort – Fast, divide-and-conquer algorithm (⚡ O(n log n) on average).
✔ MergeSort – Stable sorting, great for large datasets (O(n log n)).
✔ HeapSort – Uses a heap data structure (O(n log n)).
✔ Bubble Sort / Insertion Sort / Selection Sort – Simple but inefficient for large inputs (O(n²)).

📌 When to use?

• QuickSort for general sorting.
• MergeSort for linked lists.
• HeapSort when you need a priority queue.

2️⃣ Searching Algorithms

Finding elements quickly in data structures.

✔ Binary Search – Fast lookup in sorted arrays (O(log n)).
✔ Linear Search – Simple but slow (O(n)).
✔ Interpolation Search – Faster than Binary Search for uniformly distributed data (O(log log n)).

📌 When to use?

• Use binary search on sorted arrays for fast lookups.

3️⃣ Graph Algorithms

Used in social networks, navigation systems, and AI.

✔ Breadth-First Search (BFS) – Shortest path in unweighted graphs (O(V + E)).
✔ Depth-First Search (DFS) – Used in tree traversals and cycle detection (O(V + E)).
✔ Dijkstra’s Algorithm – Finds the shortest path in weighted graphs (O(E log V)).
✔ A Algorithm* – Pathfinding algorithm, widely used in games and maps.
✔ Floyd-Warshall Algorithm – Finds shortest paths between all pairs of nodes (O(V³)).

📌 When to use?

• BFS for shortest paths in unweighted graphs.
• Dijkstra’s for weighted graphs.
• A* for AI-driven pathfinding.

4️⃣ Dynamic Programming (DP) Algorithms

DP is key for optimization problems.

✔ Fibonacci Sequence (Memoization/Tabulation) – Classic DP example.
✔ Knapsack Problem – Used in finance, logistics, and resource allocation.
✔ Longest Common Subsequence (LCS) – Text comparison, DNA sequencing.
✔ Coin Change Problem – Solves currency exchange problems.

📌 When to use?

• Use DP when overlapping subproblems & optimal substructure exist.

5️⃣ Tree & Graph Traversal Algorithms

Essential for working with hierarchical data.

✔ Inorder, Preorder, Postorder Traversal – Used in binary trees.
✔ Trie (Prefix Tree) – Efficient for text search and autocomplete.
✔ Segment Tree / Fenwick Tree – For range queries in arrays.

📌 When to use?

• Inorder traversal for sorted output from BSTs.
• Trie for fast word lookup.

6️⃣ Greedy Algorithms

Solves optimization problems by making the best choice at each step.

✔ Huffman Coding – Used in file compression (e.g., ZIP, JPEG).
✔ Kruskal’s & Prim’s Algorithm – Finds Minimum Spanning Tree (MST) in graphs.
✔ Activity Selection Problem – Scheduling tasks efficiently.

📌 When to use?

• Greedy works best when local optimal choice leads to a global solution.

7️⃣ Hashing Algorithms

Used for fast data retrieval and security.

✔ Hash Tables (e.g., HashMap, Dictionary) – Fast lookups (O(1) on average).
✔ SHA-256, MD5 – Cryptographic hashing for passwords, security.
✔ Bloom Filters – Space-efficient way to check if an element exists.

📌 When to use?

• Use hash maps for fast key-value lookups.
• Use cryptographic hashes for security.

🚀 Conclusion

Mastering these algorithms will boost your coding efficiency, improve problem-solving skills, and prepare you for coding interviews!