Hourly DSA· 4 min read
Kadane's Algorithm: Find the Maximum Subarray Sum
Trace Kadane's algorithm through a small array. Learn when to extend or restart a subarray, why two running values matter, and how to handle negative numbers.
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DSA practice, one idea at a time.
Notes on data structures, algorithms, and finding a study routine you can stick with.
· 4 min read
Memoization vs. Tabulation: Two Ways to Evaluate a DP State
Compare memoization and tabulation through a minimum-coin example. Trace repeated states, choose dependency order, and distinguish cache storage from call-stack space.
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Dijkstra vs. BFS: When the Cheapest Path Has More Edges
Compare BFS and Dijkstra with a weighted graph. Trace distance updates, understand the min-heap and stale entries, and see why negative weights break the rule.
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Tries Explained: Follow a Prefix One Character at a Time
Understand trie prefix search with a small word set. Trace shared paths, distinguish a prefix from a complete word, and account for lookup and output costs.
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Union-Find Explained: Track Connected Components
Trace union-find through a small network. Learn what find and union mean, how undirected cycle detection works, and why balancing and path compression matter.
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Binary Search on the Answer: Find the Smallest Valid Value
Learn binary search on the answer with a shipping-capacity example. Build a monotonic feasibility check, choose safe bounds, and trace the first valid value.
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Greedy Algorithms Explained: Choices That Need Proof
Understand greedy algorithms with interval scheduling and a coin-change counterexample. Learn how an exchange argument proves a choice is safe.
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Dynamic Programming: Start by Defining the State
Understand dynamic programming with a stair-counting example. Define the state, recurrence, and base cases, then learn when memoization saves repeated work.
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Merge Intervals Explained: Sorting and Overlap Rules
Learn how to merge overlapping intervals with a sorted scan. Trace nested ranges, handle touching endpoints, and explain the time and space complexity.
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Prefix Sums Explained: Range Sums and Subarray Counts
Learn prefix sums with a range-sum example and a subarray-sum trace. Understand the extra zero, negative numbers, and when a frequency map is needed.
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Topological Sort Explained with Course Prerequisites
Learn topological sorting through course prerequisites. Trace Kahn's algorithm, understand indegrees and cycle detection, and explain the O(V + E) bound.
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Two Pointers vs. Sliding Window: Spot the Difference
Learn when to use two pointers or sliding window with worked examples, the conditions each needs, and a negative-number example that exposes a common bug.
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Linked List Cycle Detection: Slow and Fast Pointers
Understand Floyd's cycle detection with a pointer trace. Learn why slow and fast pointers meet, how to find the cycle entry, and which edge cases matter.
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BFS vs. DFS: Which Graph Traversal Should You Use?
Compare BFS and DFS with a small graph, a shortest-path example, and time and space bounds. Learn when a queue, stack, and visited set matter for correctness.
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Stack vs. Queue: LIFO, FIFO, and Interview Examples
Understand stacks and queues through operation traces, balanced brackets, and BFS. Learn when removal order matters and which complexity assumptions to state.
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Hash Map vs. Hash Set: Which Does Your Problem Need?
Compare hash maps and hash sets for duplicates, Two Sum, and frequency counting. Learn what to store, when to check it, and how to explain the complexity.
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Hourly DSA vs. AlgoExpert: Find Your Practice Rhythm
Compare Hourly DSA and AlgoExpert for coding interview prep. See where quick concept questions, video explanations, and complete coding sessions fit your day.
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Backtracking vs. Dynamic Programming: How to Choose
Compare backtracking and dynamic programming using subset sum. Learn when to explore choices, when states repeat, and why listing answers changes the cost.
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Stuck on a Coding Interview Problem? Try These Steps
Get unstuck on a coding interview problem by clarifying the output, building a tiny example, finding repeated work, and using hints without losing the learning.
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Heap Top-K Problems: When to Use a Min-Heap
Find the k largest elements with a small min-heap. Follow a worked example, compare sorting, and understand heap direction, duplicates, and complexity.
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Binary Search Boundaries: Find the First Match Safely
Understand binary search boundaries with a first-match example. Learn why left moves past mid, why right keeps mid, and how to avoid duplicate and empty-array bugs.
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Monotonic Stack: Find the Next Greater Element
Learn the monotonic stack pattern with a next-greater-element trace. Understand what gets popped, how duplicates work, and why the nested loop is O(n).
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Hourly DSA vs. Educative: Daily Practice or a Course?
Hourly DSA vs. Educative for interview prep: compare short daily questions with pattern-based coursework, plus an example of choosing the right data structure.
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How to Review DSA Mistakes Without Copying Solutions
Turn wrong DSA answers into useful review questions. Identify the missed assumption, write a tiny counterexample, and test the reasoning with a fresh variation.
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Big O Notation Explained Through Small Examples
Learn Big O notation with short examples of loops, halving, and memory. Understand why nested loops aren't always quadratic and how to explain your answer.
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A 20-Minute DSA Study Routine for Busy Days
Build a practical 20-minute DSA study routine with a quick concept check, one focused problem attempt, and a short review you can use in your next session.
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Hourly DSA vs. LeetCode: Which Fits Your Study Routine?
Compare Hourly DSA and LeetCode for coding interview prep: short pattern-recognition questions, full coding problems, and a practical way to use both together.
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Hourly DSA vs. NeetCode: Build a Daily DSA Habit
Hourly DSA vs. NeetCode for interview prep: compare quick daily practice with guided courses and coding problems, and find a routine that fits your schedule.
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