Top 10 Algorithms & Data Structures for Interviews

Whether you're prepping for your next FAANG interview or a fast-paced startup screening, knowing the right data structures and algorithms is your golden ticket. Interviewers don’t just want correct answers they want optimized thinking.

This guide walks you through the top 10 algorithms and data structures that consistently show up in coding interviews, complete with examples, visuals, and actionable insights.

🔁 1. Arrays and Strings

Why it matters:

Arrays and strings are the building blocks of most coding problems. Whether it’s two-pointer problems, sliding windows, or frequency maps you’ll see them everywhere.

Common problems:

• Two Sum
• Longest Substring Without Repeating Characters
• Rotate Image

Visual:

Array: [1, 2, 3, 4]
Index:  0  1  2  3

🔄 2. Hash Tables (aka Hash Maps)

Why it matters:

Hash tables allow constant time lookups and are key for problems that require counting, grouping, or tracking occurrences.

Common problems:

• Group Anagrams
• Top K Frequent Elements
• Valid Anagram

Pro Tip:

Combine hash maps with arrays or heaps for high-impact solutions.

🌳 3. Trees (Binary Trees, BSTs)

Why it matters:

From system design to recursion practice, trees are a goldmine. Know how to traverse them in multiple ways.

Common Traversals:

• Inorder (Left, Root, Right)
• Preorder (Root, Left, Right)
• Postorder (Left, Right, Root)

Common problems:

• Maximum Depth of Binary Tree
• Validate Binary Search Tree
• Lowest Common Ancestor

📊 4. Heaps (Priority Queues)

Why it matters:

When you need to get the smallest/largest elements efficiently, heaps are your go-to. Min-heaps and max-heaps are used in real-world scheduling systems and search engines.

Common problems:

• Merge K Sorted Lists
• Find Median from Data Stream
• Top K Frequent Words

Pseudocode:

import heapq
heapq.heappush(min_heap, value)

🔗 5. Linked Lists

Why it matters:

Linked lists teach you memory management and pointer manipulation — key in low-level interviews and C-based languages.

Common problems:

• Reverse Linked List
• Detect Cycle in Linked List
• Merge Two Sorted Lists

Visual:

[1] -> [2] -> [3] -> [4] -> None

📐 6. Recursion & Backtracking

Why it matters:

These are essential for solving problems where decisions branch in multiple directions (e.g., permutations, combinations).

Common problems:

• Subsets
• Permutations
• N-Queens

Framework:

def backtrack(path, choices):
    if goal:
        result.append(path)
    for choice in choices:
        backtrack(path + [choice], choices - {choice})

🧭 7. Graphs (BFS/DFS)

Why it matters:

Graphs are core to real-world problems — from maps and social networks to dependency resolution.

Traversal Types:

• Breadth-First Search (BFS): Level-order
• Depth-First Search (DFS): Explore deeply first

Common problems:

• Number of Islands
• Clone Graph
• Course Schedule

Graph Representation:

graph = {
  "A": ["B", "C"],
  "B": ["D"],
  "C": ["E"],
}

⛓️ 8. Stacks & Queues

Why it matters:

These linear structures simulate real-world processes: browsers (stacks), print queues (queues), and undo-redo systems.

Common problems:

• Valid Parentheses
• Min Stack
• Implement Queue using Stacks

📦 9. Sliding Window

Why it matters:

A powerful pattern used to reduce time complexity in problems involving subarrays and substrings.

Common problems:

• Maximum Sum Subarray of Size K
• Longest Substring Without Repeating Characters
• Minimum Window Substring

Example:

window_start = 0
for window_end in range(len(array)):
    # Expand window
    # Shrink when needed

🔄 10. Dynamic Programming (DP)

Why it matters:

DP problems are notorious but conquerable. They test your understanding of overlapping subproblems and optimal substructure.

Common problems:

• Fibonacci Numbers
• Longest Increasing Subsequence
• 0/1 Knapsack

Top-down (Memoization) vs Bottom-up (Tabulation)

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🚀 Final Thoughts

These 10 topics make up the core of 90% of coding interview questions. But the goal isn’t just memorization — it’s mastery. Focus on patterns, problem-solving, and time/space complexity trade-offs.

✅ Pro Tips for Interview Prep:

• Practice on LeetCode, HackerRank, or Codeforces.
• Time yourself.
• Review not just the solution, but why it's optimal.
• Talk through problems as if you're in a live interview.

💬 Let’s Discuss!

Which of these do you struggle with most?

What’s your favorite algorithm trick that helped you crack an interview?

Drop your thoughts in the comments — let’s learn together! 💡