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🧩 Data Structures & Algorithms

Master DataStructures &Algorithms.

DSA is the foundation of problem solving in software engineering. From product-based companies to startups, strong DSA skills are the deciding factor for landing high-paying tech roles.

View DSA Roadmap 🗺️500+ Problems →
dsa_journey.py
# Your DSA Journey
 
skills = {
"arrays": True,
"trees": True,
"graphs": True,
"dp": True
}
 
def get_offer(skills):
if all(skills.values()):
return "🏆 Dream Offer"
 
get_offer(skills)
# 🚀 Start now!
Interview Ready
⚡ Optimised Code
Data Structures
You Must Know

Each data structure solves a specific category of problems. Master them all.

📊
45 Problems
Arrays
Index-based storage. O(1) access. Foundation of all DSA.
TraversalSortingSliding Window
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🔤
38 Problems
Strings
Character sequences. Pattern matching, anagrams, substrings.
Two PointersHashingKMP
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🔗
32 Problems
Linked List
Dynamic chaining. Reversal, cycle detection, merging.
Fast/SlowReversalMerge
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📚
28 Problems
Stack
LIFO. Balanced brackets, next greater element, monotonic stack.
LIFOMonotonicParsing
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🚶
20 Problems
Queue
FIFO. BFS implementation, sliding window maximum.
FIFOBFSDeque
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#️⃣
40 Problems
Hash Table
O(1) lookup. Frequency counting, two-sum, grouping.
O(1) LookupFrequencyGrouping
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🌲
55 Problems
Trees
Hierarchical structure. DFS, BFS, LCA, path problems.
DFSBFSRecursion
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🕸️
60 Problems
Graphs
Nodes + edges. BFS, DFS, shortest path, cycle detection.
BFS/DFSDijkstraTopological
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🏔️
22 Problems
Heap
Min/Max priority queue. Top-K problems, median finding.
Priority QueueTop-KMedian
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🌐
18 Problems
Trie
Prefix tree. Autocomplete, word search, IP routing.
PrefixAutocompleteSearch
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15+ Patterns That Crack
90% of Interview Problems

These are the techniques top engineers use. Master the patterns, not just the problems.

👈👉
O(n)
Two Pointers
Two indices moving toward each other or in the same direction. Perfect for sorted arrays and palindrome checks.
Example: Valid Palindrome, 3Sum, Container With Most Water
🪟
O(n)
Sliding Window
Maintain a window of elements and slide it across. Avoids recalculating results for overlapping subarrays.
Example: Max Subarray Sum, Longest Substring Without Repeat
🔍
O(log n)
Binary Search
Halve the search space on every step. Applies to any monotonic function, not just sorted arrays.
Example: Search in Rotated Array, Median of Two Sorted Arrays
➕
O(1) query
Prefix Sum
Precompute cumulative sums to answer range queries in O(1). Pairs with hashing for subarray problems.
Example: Subarray Sum Equals K, Range Sum Query
🐢🐇
O(n)
Fast & Slow Pointers
Floyd's cycle detection. One pointer moves twice as fast. Detects cycles and finds midpoints.
Example: Linked List Cycle, Find Middle of List
🌊
O(V+E)
BFS / Level Order
Explore layer by layer using a queue. Best for shortest path in unweighted graphs.
Example: Binary Tree Level Order, Word Ladder
🌀
O(V+E)
DFS / Recursion
Go deep before wide. Uses a stack (implicit or explicit). Foundation for tree traversals.
Example: Path Sum, Number of Islands, Clone Graph
💡
Varies
Dynamic Programming
Break into overlapping subproblems and store results. The hardest pattern but unlocks the highest-value problems.
Example: Longest Common Subsequence, 0/1 Knapsack, Coin Change
↩️
O(n!)
Backtracking
Explore all possibilities and undo choices that don't work. For permutations, combinations.
Example: N-Queens, Sudoku Solver, Word Search
🎯
O(n log n)
Greedy
Make the locally optimal choice at each step. Requires proof local optimum leads to global optimum.
Example: Activity Selection, Jump Game, Huffman Coding
🔀
O(α(n))
Union Find
Track connected components with near-constant union and find.
Example: Number of Connected Components, Redundant Connection
📈
O(n)
Monotonic Stack
Maintain a stack in increasing or decreasing order. Solves next greater/smaller element problems.
Example: Next Greater Element, Daily Temperatures, Largest Rectangle
📅
O(n log n)
Merge Intervals
Sort intervals and merge overlapping ones. Apply to scheduling, calendar, and range problems.
Example: Merge Intervals, Insert Interval, Meeting Rooms
📋
O(V+E)
Topological Sort
Order nodes in a DAG such that every edge goes left to right. For dependency resolution.
Example: Course Schedule, Alien Dictionary, Task Scheduler
Time & Space
Complexity Cheatsheet

Understand Big-O notation to write solutions that scale.

NotationNameExampleSpeed
O(1)Constant⚡ Best
O(log n)Logarithmic🟢 Great
O(n)Linear🔵 Good
O(n log n)Linearithmic🟡 Acceptable
O(n²)Quadratic🟠 Slow
O(2ⁿ)Exponential🔴 Avoid
O(n!)Factorial💀 Worst
💡
Always analyse before you code
Before writing a single line, ask: What is the time and space complexity of my approach?
🎯
Optimal ≠ fastest always
Sometimes O(n log n) with simple code beats O(n) with complex logic. Readability matters.