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THE ULTIMATE DSA GUIDE
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
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3. Hash
Read our Tables
Privacy policy (Hash
and Terms of use Maps/Sets)
for more information.
Implementation not needed. Just379
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:
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Breadth-First Search (BFS)
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Depth-First Search (DFS)
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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.
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3. Greedy Algorithms
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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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