Series
Data Structures and Algorithms
Master the core of computer science. This series demystifies Data Structures and Algorithms, from Big O to graph theory. Learn to write efficient, scalable code and ace your technical interviews with confidence.
22
Articles
6h 7m
Estimated reading
Intermediate to Advanced
Knowledge level
279
Readers
About this series
Master the core of computer science. This series demystifies Data Structures and Algorithms, from Big O to graph theory. Learn to write efficient, scalable code and ace your technical interviews with confidence.
Series Progress
0% Complete0 of 22 articles viewed
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Who is this for?
Software engineers and developers learning this topic.
Knowledge Level
Intermediate to Advanced
Last Updated
May 25, 2026
Created by
Abstract Algorithms
All Articles
Article 1
Mastering Binary Tree Traversal: A Beginner's Guide
TLDR: Binary tree traversal is about visiting every node in a controlled order. Learn pre-order, in-order, post-order, and level-order, and you can solve many interview and production problems cleanly
15 min read
Article 2
Tree Data Structure Explained: Concepts, Implementation, and Interview Guide
TLDR: Trees are hierarchical data structures used everywhere — file systems, HTML DOM, databases, and search algorithms. Understanding Binary Trees, BSTs, and Heaps gives you efficient \(O(\log N)\) s
15 min read
Article 3
The Ultimate Data Structures Cheat Sheet
TLDR: Data structures are tools. Picking the right one depends on what operation you do most: lookup, insert, delete, ordered traversal, top-k, prefix search, or graph navigation. Start from operation
15 min read
Article 4
Exploring Backtracking Techniques in Data Structures
TLDR: Backtracking is "Recursion with Undo." You try a path, explore it deeply, and if it fails, you undo your last decision and try the next option. It explores the full search space but prunes inval
13 min read
Article 5
Exploring Different Types of Binary Trees
TLDR: A Binary Tree has at most 2 children per node, but the shape of the tree determines performance. A Full tree has 0 or 2 children. A Complete tree fills left-to-right. A Perfect tree is a symmetr
12 min read
Article 6
K-Way Merge Pattern: Merge K Sorted Sequences with a Min-Heap
TLDR: K-Way Merge uses a min-heap with exactly one entry per sorted input list. Each entry stores the current element's value plus the coordinates to find the next element in that list. Pop the minimu
16 min read
Article 7
Top K Elements Pattern: Find the Best K Without Sorting Everything
TLDR: To find the top K largest elements, maintain a min-heap of size K. For every new element, push it onto the heap. If the heap exceeds K, evict the minimum. After processing all N elements, the he
14 min read
Article 8
Two Heaps Pattern: Find the Median of a Data Stream Without Sorting
TLDR: Two Heaps partitions a stream into two sorted halves. A max-heap holds everything below the median; a min-heap holds everything above it. Keep the heaps size-balanced and you can read the median
15 min read
Article 9
BFS — Breadth-First Search: Level-by-Level Graph Exploration
TLDR: BFS explores a graph level by level using a FIFO queue, guaranteeing the shortest path in unweighted graphs. Recognize BFS problems by keywords: "shortest path," "minimum steps," or "level order
16 min read
Article 10
Binary Search Patterns: Five Variants Every Senior Engineer Knows
TLDR: Binary search has five patterns beyond the classic "find the target": leftmost position, rightmost position, rotated array search, minimum in rotated array, and 2D matrix search. The root of eve
17 min read
Article 11
Cyclic Sort: Find Missing and Duplicate Numbers in O(n) Time, O(1) Space
TLDR: If an array holds n numbers in range [1, n], each number belongs at index num - 1. Cyclic sort places every element at its correct index in O(n) time using O(1) space — then a single scan reveal
16 min read
Article 12
DFS — Depth-First Search: Go Deep Before Going Wide
TLDR: DFS explores a graph by diving as deep as possible along each path before backtracking, using a call stack (recursion) or an explicit stack. It is the go-to algorithm for cycle detection, path f
15 min read
Article 13
Fast and Slow Pointer: Floyd's Cycle Detection Algorithm Explained
TLDR: Move a slow pointer one step and a fast pointer two steps through a linked structure. If they ever meet, a cycle exists. Then reset one pointer to the head and advance both one step at a time —
17 min read
Article 14
In-Place Reversal of a Linked List: The 3-Pointer Dance Every Interviewer Expects
TLDR: Reversing a linked list in O(1) space requires three pointers — prev, curr, and next. Each step: save next, flip curr.next to point backward, advance both prev and curr. Learn this once and you
16 min read
Article 15
Merge Intervals Pattern: Solve Scheduling Problems with Sort and Sweep
TLDR: Sort intervals by start time, then sweep left-to-right and merge any interval whose start ≤ the current running end. O(n log n) time, O(n) space. One pattern — three interview problems solved.
13 min read
Article 16
Sliding Window Technique: From O(n·k) Scans to O(n) in One Pass
TLDR: Instead of recomputing a subarray aggregate from scratch on every shift, maintain it incrementally — add the incoming element, remove the outgoing element. For a fixed window this costs O(1) per
16 min read
Article 17
Tries (Prefix Trees): The Data Structure Behind Autocomplete
TLDR: A Trie stores strings character by character in a tree, so every string sharing a common prefix shares those nodes. Insert and search are O(L) where L is the word length. Tries beat HashMaps on
17 min read
Article 18
Two Pointer Technique: Solving Pair and Partition Problems in O(n)
TLDR: Place one pointer at the start and one at the end of a sorted array. Move them toward each other based on a comparison condition. Every classic pair/partition problem that naively runs in O(n²)
16 min read
Article 19
Big O Notation Explained: Time Complexity, Space Complexity, and Why They Matter in Interviews
TLDR: Big O notation describes how an algorithm's resource usage grows as input size grows — not how fast it runs on your laptop. Learn to identify the 7 complexity classes (O(1) through O(n!)), deriv
34 min read
Article 20
Bloom Filters Explained: Membership Testing with Zero False Negatives
TLDR: A Bloom filter is a bit array of m bits + k independent hash functions that sets k bits on insert and checks those same k bits on lookup. If any checked bit is 0, the element is definitely not i
19 min read
Article 21
Count-Min Sketch Explained: Frequency Estimation at Streaming Scale
TLDR: Count-Min Sketch (CMS) is a fixed-size d × w counter matrix that estimates how often any element has appeared in a stream. Insert: hash the element with each of the d hash functions to get one c
22 min read
Article 22
HyperLogLog Explained: Counting Billions of Unique Items with 12 KB
TLDR: HyperLogLog estimates the number of distinct elements in a dataset using ~12 KB of memory regardless of cardinality — with ±0.81% error. The insight: if you hash every element to a random bit st
18 min read
Data Structures and Algorithms: Learning Roadmap
You're staring at your calendar. There's a coding interview next week, or maybe next month. You open LeetCode and immediately feel overwhelmed by the sea of "Hard" problems. You've heard about data structures and algorithms but don't know where to start or what order makes sense.
That overwhelm is exactly why most engineers struggle with DSA. It's not that the concepts are impossibly complex — it's that traditional resources throw you into the deep end without building the foundational intuition first. This roadmap fixes that by giving you a clear decision tree for learning DSA based on your timeline and current knowledge level.
TLDR: This roadmap guides you through the Data Structures and Algorithms series with three learning paths based on your timeline and experience — complete beginner (3+ months), know basics (3 months), or interview fast-track (1 month).
What You'll Learn
Understand Data Structures and Algorithms through real published examples
Follow a sequence of 22 articles from fundamentals to deeper topics
Connect related concepts: algorithms, data structures, interview-prep
Practice explaining trade-offs and implementation decisions
Prerequisites
FAQs
How should I read this series?
Start from the first article if you are new, or use the article list to jump into the most relevant topic.
Is progress automatic?
Progress is based on articles opened from this browser using the local learning history.