Data Structures and Algorithms (DSA)

Data Structures and Algorithms (DSA) are the foundation of efficient problem-solving in programming. Whether you're preparing for coding interviews or building high-performance software, mastering DSA helps you write optimized and scalable code.

Why Learn DSA?

  • Builds strong problem-solving and logical thinking skills.
  • Essential for cracking technical interviews at top companies.
  • Improves your ability to write optimized, maintainable code.
  • Forms the core of competitive programming and coding contests.

Prerequisites

You should be familiar with the basics of any programming language (JavaScript, Python, C++, etc.) such as variables, loops, functions, and basic syntax. If you're new to coding, learn these first.

Which Language Should I Use?

DSA can be practiced in any language. At JDCodebase, we focus on JavaScript to make it easy for web developers to master problem-solving without switching languages.

How to Learn DSA?

  • Understand the core concept of each data structure or algorithm.
  • Practice problems daily (start from easy, move to medium and hard).
  • Learn patterns like Two Pointer, Sliding Window, Binary Search, etc.
  • Dry run your code and analyze time and space complexity.
  • Watch video explanations and revise with notes or slides.

Quick Overview of DSA Topics

Time and Space Complexity

Analyzes the performance of algorithms in terms of runtime and memory usage. Helps in choosing the most efficient solution for a problem.

Math

Covers fundamental mathematical concepts like GCD, LCM, primes, and modular arithmetic. Often used in algorithm optimization and problem-solving.

Bit Manipulation

Involves operations on binary representations using AND, OR, XOR, etc. Useful for solving problems with space and performance constraints.

Array

A linear data structure used to store elements in contiguous memory. Foundation for many algorithmic techniques and problems.

String

Focuses on character arrays and operations like searching, matching, and manipulation. Key topic in interviews and text processing tasks.

Two Pointer

Uses two indices moving through data to solve problems efficiently. Common in array and string pattern problems.

Sliding Window

Optimizes subarray or substring problems by reusing results in a fixed-size or dynamic window. Reduces time complexity significantly.

Hash Table

Stores key-value pairs with constant time access. Powerful for frequency counting, indexing, and lookups.

Stack

A LIFO (Last In, First Out) structure used in parsing, backtracking, and expression evaluation. Supports push and pop operations.

Queue

A FIFO (First In, First Out) structure useful in BFS, scheduling, and stream processing. Comes in variants like circular and priority queues.

Linked List

A sequential data structure with nodes connected by pointers. Useful for dynamic memory allocation and efficient insertions/deletions.

Prefix Sum

Preprocessing technique to speed up range queries. Commonly used in subarray sum and difference problems.

Binary Search

Efficient algorithm for finding elements in sorted arrays in O(log n) time. Also used in search space problems.

Recursion

A technique where a function calls itself to solve smaller subproblems. Forms the base for divide-and-conquer strategies.

Backtracking

Explores all potential solutions and undoes choices when needed. Used in problems like permutations, combinations, and Sudoku.

Sorting

Organizes elements in a specific order. Includes bubble, merge, quick, and radix sort with varying time/space trade-offs.

Greedy Algorithms

Makes the locally optimal choice at each step for a global solution. Efficient for problems like interval scheduling and coin change.

Trees

Hierarchical data structures with parent-child relationships. Used in organizing data, file systems, and decision processes.

Binary Tree

Each node has up to two children. Fundamental for tree traversal techniques and many hierarchical problems.

Binary Search Tree

A binary tree where left < root < right. Enables fast lookup, insertion, and deletion in O(log n) time.

Trie

A tree-based structure for efficient string search and prefix matching. Commonly used in autocomplete systems.

Heap

A complete binary tree used to implement priority queues. Useful for scheduling, Dijkstra's algorithm, and more.

Graph

A collection of nodes (vertices) connected by edges. Represents networks, paths, and relationships.

Graph Traversal

Techniques like DFS and BFS to explore nodes. Forms the basis for many graph-based algorithms.

Topological Sort

Orders nodes in a directed acyclic graph (DAG). Used in task scheduling and dependency resolution.

Union Find

A disjoint set data structure for managing connected components. Commonly used in Kruskal’s algorithm and cycle detection.

Dynamic Programming

Solves problems by breaking them into overlapping subproblems. Stores intermediate results to avoid recomputation.

Memoization & Tabulation

Memoization is top-down with caching; tabulation is bottom-up. Both are core techniques in dynamic programming.

1D 2D DP

Dynamic programming in one or two dimensions. Used in problems like knapsack, grid paths, and matrix chains.

Bitmask DP

Uses bitmasks to represent states in DP problems. Efficient for subsets, permutations, and TSP-like problems.

DP on Trees

Applies dynamic programming to tree structures. Used in counting paths, independent sets, and subtree problems.

DP on Graphs

Combines graph traversal with DP to solve shortest path, DAG-based, and constraint optimization problems.

Segment Tree

A binary tree structure for range queries and updates. Supports efficient min, max, sum operations on intervals.

Monotonic Stack

A stack that maintains increasing or decreasing order. Used in histogram, next greater/smaller element problems.

Advanced Graph Algorithms

Includes Dijkstra, Floyd-Warshall, Bellman-Ford, etc. Solves shortest path, flows, and network-related problems.

Game Theory

Analyzes strategic interactions and winning strategies. Covers concepts like Grundy numbers and minimax.

Start Solving!

Head over to specific DSA topics from the sidebar or below and start solving problems with explanations, code, and dry runs. Let's level up your coding skills together 🚀