DSA Concepts

Master the fundamentals of Data Structures & Algorithms.
Build a strong foundation before tackling patterns and problems.

DSA Learning Roadmap
22

Foundations2Core concepts every developer needs

Easy

Big O Notation

Measuring algorithm efficiency

5 key points6 examples
Easy

Binary System & Bit Manipulation

Base-2 representation and bitwise operations

6 key points3 examples

Data Structures8Building blocks for algorithms

Easy

Arrays

Contiguous memory, indexed access

6 key points6 examples
Easy

Hash Tables

O(1) key-value lookup

6 key points6 examples
Easy

Stacks

Last-In-First-Out (LIFO)

6 key points6 examples
Easy

Queues

First-In-First-Out (FIFO)

6 key points6 examples
Med

Heaps & Priority Queues

Fast min/max access with efficient updates

6 key points3 examples
Med

Trees

Hierarchical data and recursion

7 key points3 examples
Easy

Trie (Prefix Tree)

Fast prefix lookup and word-based search

6 key points2 examples
Easy

Linked Lists

Nodes connected by pointers

6 key points6 examples

Algorithms5Common algorithm techniques

Easy

Graphs

Nodes, edges, traversal, and reachability

7 key points3 examples
Med

Recursion

Think smaller, solve, then combine

5 key points3 examples
Hard

Backtracking

Try options, prune, and backtrack cleanly

5 key points2 examples
Med

Sorting Algorithms

Arranging elements efficiently

8 key points4 examples
Hard

Dynamic Programming

Breaking problems into overlapping subproblems

8 key points3 examples

Patterns1Reusable problem-solving patterns

Med

Sliding Window

Efficiently processing contiguous subarrays and substrings

7 key points3 examples

Algorithm Patterns6Step-by-step visualizations of common algorithm patterns

Med

Two Pointers

Use two pointers to traverse an array or string, reducing time complexity from O(n^2) to O(n) for problems involving pairs or subarrays.

Time: O(n)3 variants
Med

Hash Map

Use a hash map for O(1) lookup to track seen elements, frequencies, or complement values during array traversal.

Time: O(n)3 variants
Med

Bit Manipulation

Use bitwise operations to solve problems efficiently by treating integers as arrays of bits.

Time: O(1) or O(log n)3 variants
Med

Binary Search

Eliminate half the search space each iteration by comparing the midpoint against a condition, reducing O(n) linear scans to O(log n).

Time: O(log n)4 variants
Med

Sorting

Use sorting as a preprocessing step to simplify problems, or apply partition logic for in-place rearrangement. Many interview problems become trivial once the array is sorted.

Time: O(n log n)4 variants
Med

Heap

Use a heap (priority queue) when you repeatedly need the minimum or maximum of a changing set while also inserting and deleting values efficiently.

Time: O(log n)3 variants