https://github.com/punitkatiyar/javascript-with-dsa
JavaScript with DSA (Data Structures and Algorithms) Roadmap
https://github.com/punitkatiyar/javascript-with-dsa
ecmascript javascript node nodemon
Last synced: 3 months ago
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JavaScript with DSA (Data Structures and Algorithms) Roadmap
- Host: GitHub
- URL: https://github.com/punitkatiyar/javascript-with-dsa
- Owner: punitkatiyar
- License: mit
- Created: 2025-01-14T12:06:04.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-14T12:15:42.000Z (6 months ago)
- Last Synced: 2025-01-14T13:23:40.304Z (6 months ago)
- Topics: ecmascript, javascript, node, nodemon
- Homepage: https://punitkatiyar.github.io/javascript-with-dsa/
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# JavaScript with DSA (Data Structures and Algorithms) Roadmap
to help you build strong foundations in both JavaScript and problem-solving skills:
## 1. Learn the Basics of JavaScript
Before diving into DSA, ensure a solid understanding of JavaScript fundamentals.
### Topics to Cover:
> Syntax and Basics:
> Variables (var, let, const)
> Data Types (Strings, Numbers, Booleans, etc.)
> Operators (Arithmetic, Logical, Comparison)
> Conditional Statements (if-else, switch)
> Loops (for, while, do-while)
> Functions:
> Function Declaration and Expression
> Arrow Functions
> Callback Functions
> Objects and Arrays:
> Object Properties and Methods
> Array Methods (map, filter, reduce, forEach)
> ES6+ Features:
> Template Literals
> Destructuring
> Spread and Rest Operators
> Promises and Async/Await
## 2. Understand Big O Notation
Big O notation is essential to evaluate the time and space complexity of algorithms.
### Topics to Cover:
- What is Big O?
- Common Complexities:
- O(1) - Constant Time
- O(log n) - Logarithmic Time
- O(n) - Linear Time
- O(n²) - Quadratic Time
- Space Complexity Analysis
## 3. Learn Core Data Structures
Familiarize yourself with the key data structures and their operations in JavaScript.
### Data Structures to Learn:
#### Arrays
- Basics (Insertion, Deletion, Traversal)
- Sorting (Bubble, Selection, Merge, Quick Sort)
- Searching (Linear Search, Binary Search)
- Two-pointer Techniques
- Strings
- Substrings, Palindromes
- String Manipulation (Reversal, Anagrams, etc.)
#### Stacks and Queues
- Implement using arrays or linked lists
- Applications: Balanced Parentheses, Min Stack
#### Linked Lists
- Singly and Doubly Linked Lists
- Operations: Insertion, Deletion, Reversal
#### Hashing
- Hash Maps (Map in JavaScript)
- Hash Tables
- Applications: Frequency Counter Pattern
#### Trees
Binary Trees
Binary Search Trees
Tree Traversals (Inorder, Preorder, Postorder)
#### Graphs
- Representation (Adjacency List, Matrix)
- BFS and DFS Traversals
- Applications: Shortest Path, Cycle Detection
#### Heaps
- Min-Heap, Max-Heap
- Priority Queues
- Tries (Prefix Trees)
- Word Search, Auto-complete Applications
- Dynamic Programming (DP)
#### Memoization and Tabulation
- Common Problems: Fibonacci, Knapsack, Longest Common Subsequence
## Master Important Algorithms
Learn the fundamental algorithms used in solving problems efficiently.
### Algorithm Categories:
#### Sorting Algorithms:
- Bubble Sort, Merge Sort, Quick Sort
#### Searching Algorithms:
- Binary Search, Depth-First Search (DFS), Breadth-First Search (BFS)
#### Recursion:
- Factorials, Fibonacci Series
#### Backtracking:
- N-Queens, Sudoku Solver
#### Greedy Algorithms:
- Activity Selection, Huffman Coding
#### Divide and Conquer:
- Merge Sort, Quick Sort
#### Dynamic Programming:
- Coin Change, Longest Increasing Subsequence
#### Graph Algorithms:
- Dijkstra’s, Prim’s, Kruskal’s
#### Sliding Window & Two Pointers:
- Maximum Sum Subarray, Longest Substring Without Repeating Characters
```
5. Practice Problem-Solving
Start solving problems on platforms like LeetCode, HackerRank, Codewars, or GeeksforGeeks.
Steps to Practice:
Start with easy problems (arrays, strings, simple loops).
Gradually move to medium problems (sliding window, recursion).
Take on hard problems once confident.
Focus on:
Optimization of time and space.
Understanding edge cases.
6. Projects to Reinforce DSA with JavaScript
Apply DSA concepts in practical projects to solidify your understanding.
Suggested Projects:
Task Manager with Priority Queue
Use heaps or a custom priority queue.
Autocomplete Feature
Use Tries for prefix searching.
Social Network Graph
Represent connections using graphs and implement BFS/DFS.
File Organizer
Apply sorting algorithms and hash maps.
7. Explore Advanced Topics (Optional)
Once you’re confident, explore:
Functional Programming in JavaScript.
Web Development Algorithms (e.g., DOM Manipulation Optimization).
Concurrency Models (Event Loop, Async/Await).
Tools and Resources:
Books:
Eloquent JavaScript by Marijn Haverbeke
Grokking Algorithms by Aditya Bhargava
Platforms:
freeCodeCamp
LeetCode
HackerRank
Visual Tools:
VisuAlgo
Would you like me to help create a weekly study plan based on this roadmap?
```