🚀 DSA Master

Welcome, Interview Masters!

Today we have curated a list of the Top Data Structures and Algorithms to study for interviews.

These concepts will teach you the core techniques you need to know to crush any technical interview.

Algorithms are divided into 2 broad categories:

1. Data Structure specific algorithms

2. General algorithms/techniques

Implement all the algorithms unless otherwise specified.

We will be using some of these algorithms in next editions of Interview Master (if not already covered in the previous editions).

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Data Structure specific algorithms

1. Arrays

• Sorting:

  • QuickSort: Efficient average-case time complexity (O(nlog n))

  • MergeSort: Stable sort, useful when order matters (O(nlog n))

• Searching:

  • Binary Search: Fast search in sorted arrays (O(log n))

• Two Pointers:

  • In-place manipulation, often for sorted arrays (e.g., removing duplicates)

• Sliding Window:

  • Subarray problems, finding maximum/minimum within a window

2. Linked Lists

• Traversal:

  • Iterate through the list, understand the node structure

• Insertion/Deletion:

  • At beginning, end, or at a specific position

• Reversal:

  • In-place reversal, recursive and iterative approaches

• Cycle Detection:

  • Floyd's Tortoise and Hare algorithm

3. Hash Tables (Hash Maps/Sets)

• Implementation not needed. Just understand following:

  • Understand how hash functions work

  • Insertion/Deletion/Lookup

  • Collision Handling

4. Trees (Binary Trees, Binary Search Trees, etc.)

• Traversal:

  • Inorder, Preorder, Postorder (recursive and iterative)

• Searching:

  • Find a node with a given value (especially in BSTs)

5. Stacks

• Implementation not needed. Just understand following:

  • Push/Pop/Peek Operations

6. Queues

• Implementation not needed. Just understand following:

  • Enqueue/Dequeue Operations

7. Heaps (Priority Queues)

• Implementation not needed. Just understand following:

  • Insertion/Deletion (extract-min/max)

  • Building a Heap

• Top K Elements:

  • Using a heap to find k largest/smallest elements

8. Graphs

• Traversal:

  • Breadth-First Search (BFS)

  • Depth-First Search (DFS)

• Shortest Path:

  • Dijkstra's Algorithm

• Cycle Detection:

  • DFS

9. Tries

• Implement Trie from scratch

• Insertion/Searching:

  • For words/prefixes

• Autocompletion:

  • Using a trie for word suggestions

10. Union-Find (Disjoint Set)

• Implement Union-Find from scratch

• Find/Union Operations

• Cycle Detection in undirected graphs

General algorithms/techniques

1. Recursion

• Defining a problem in terms of itself, often leading to elegant and concise solutions.

• Solve: Factorial calculation, tree traversals, depth-first search.

2. Dynamic Programming

• Breaking down a problem into overlapping subproblems and storing solutions to avoid recomputation.

• Solve: Fibonacci sequence, Knapsack problem, Longest Common Subsequence.

3. Greedy Algorithms

• Making locally optimal choices at each step with the hope of finding a global optimum.

• Implement: Kruskal's algorithm for minimum spanning trees.

4. Backtracking

• Incrementally building solutions, exploring all possible paths, and abandoning invalid ones.

• Solve: Sudoku solver, N-Queens problem, generating permutations.

WHAT'S NEXT?

Once you have implemented the above algorithms, solve Interview Master 100, which contains top 100 interview problems.

Each problem builds upon previous problems so that you can gradually expand your knowledge as you progress.

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Until next time, take care! 🚀

Cheers,

Sahil + Sarra