Data Structures & Algorithms
Master the fundamentals of computer science. Learn Big-O notation, sorting algorithms, searching techniques, and essential data structures like trees, graphs, hash tables, and linked lists. These concepts are language-agnostic and essential for technical interviews.
Practice by Category
Big O
6 exercises
Sorting
6 exercises
Searching
6 exercises
Linked Lists
6 exercises
Stacks Queues
6 exercises
Trees
6 exercises
Hash Tables
6 exercises
Graphs
6 exercises
Recursion
6 exercises
What You'll Learn
Big-O time and space complexity analysis
Sorting algorithms (bubble, merge, quick sort)
Searching algorithms (linear, binary search)
Linked lists and their operations
Stacks, queues, and priority queues
Trees, BSTs, and tree traversals
Hash tables and collision handling
Graphs, BFS, and DFS
Recursion and dynamic programming
Frequently Asked Questions
Why are data structures and algorithms important?
DSA forms the foundation of efficient programming. Understanding them helps you write faster, more memory-efficient code, and they are heavily tested in technical interviews at most tech companies.
What is Big-O notation?
Big-O notation describes how the runtime or space requirements of an algorithm grow as the input size increases. O(1) is constant time, O(n) is linear, O(log n) is logarithmic, and O(n²) is quadratic.
Which sorting algorithm should I learn first?
Start with simple algorithms like bubble sort to understand the concept, then learn efficient ones like merge sort and quick sort. Understanding their trade-offs (time vs space, stability) is more important than memorizing code.
Ready to Master Data Structures & Algorithms?
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