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Data Structures & Algorithms for Interviews
Crack FAANG and top-tier coding rounds by mastering patterns, not memorizing solutions.
CREATED BY
A
Arnav K. [PLACEHOLDER] ★ 5.0
SSE at MegaMart Tech | 8+ years of experience
About this Path
Designed for working engineers who want to break into product companies with rigorous coding screens. Rather than grinding hundreds of random problems, this roadmap teaches the 15 core patterns that cover 80% of interview questions. You will walk into any LeetCode-style round with a repeatable problem-solving framework.
Path Overview
Intermediate LevelCertificate of CompletionAbout 45 hours to completeEnglish language18+ curated videosLearn online at your own pace6 modules with resourcesGamified & interactive
Path Curriculum
The UMPIRE method: Understand, Match, Plan, Implement, Review, Evaluate
A six-step framework that prevents blank-page panic and signals seniority to interviewers.
Big-O analysis: time and space from first principles
Deriving complexity from loops, recursion trees, and amortized cost of dynamic arrays.
Choosing the right data structure for a constraint set
HashMap vs TreeMap, deque vs heap—matching data structures to access-pattern requirements.
Two-pointer and sliding window patterns
Fixed and variable windows, fast-slow pointers, and in-place array manipulation.
HashMap and HashSet: frequency counting and anagram problems
Character maps, sliding window with maps, and design problems requiring O(1) lookup.
Prefix sums and difference arrays
Range sum queries, subarray sum equals K, and interval update optimizations.
Binary search on answer space
Applying binary search to non-array problems: capacity, minimum-maximum, and allocation.
Linked list manipulation: reverse, cycle detection, merge
Floyd's cycle algorithm, k-group reversal, and merging sorted lists.
Monotonic stack for next-greater and histogram problems
Largest rectangle in histogram, daily temperatures, and span calculations.
Deque and sliding window maximum
O(n) window max using deque, LRU cache with doubly linked list and HashMap.
Binary tree traversals and their interview patterns
Iterative DFS with explicit stack, Morris traversal, and level-order BFS.
Binary search trees: validation, successor, and LCA
In-order property exploitation, lowest common ancestor, and balanced BST construction.
Graph traversal: BFS, DFS, and cycle detection
Adjacency list representation, visited sets, and directed vs undirected cycle checks.
Topological sort and Dijkstra's shortest path
Kahn's algorithm for course scheduling, priority queue Dijkstra, and Bellman-Ford edge cases.
Heap patterns: top-K, K-way merge, and median stream
Python heapq usage, two-heap median maintenance, and K sorted lists merge.
Trie implementation and prefix search problems
Insert, search, startsWith, and autocomplete with frequency-ranked results.
Interval scheduling: merge, insert, and non-overlapping
Sorting by start, sweep-line technique, and minimum rooms for meeting problems.
1D DP: Fibonacci, climbing stairs, house robber variants
Memoization to tabulation transition, state compression to O(1) space.
2D DP: grids, substrings, and knapsack problems
Unique paths, longest common subsequence, and 0/1 knapsack state definition.
DP on trees and intervals
House robber III on trees, matrix chain multiplication, and burst balloons.
Mock coding screen: timed set with debrief
Four problems across patterns, live solutions recorded with complexity walk-through.
What you'll learn
- ✓Identify the right algorithm pattern within the first two minutes of reading a problem statement.
- ✓Implement sliding window, two-pointer, and binary search solutions efficiently in Python or Java.
- ✓Traverse and manipulate trees and graphs using BFS, DFS, and topological sort with confidence.
- ✓Apply dynamic programming by recognizing overlapping subproblems and formulating recurrence relations.
- ✓Analyse time and space complexity precisely and communicate Big-O trade-offs to interviewers.
- ✓Manage interview pressure using a structured think-aloud approach that earns partial credit on hard problems.