Data Structures and Algorithms for Inrerviews

A structured collection of Data Structures and Algorithms problems, solutions, notes, and interview-preparation material.

This repository is a study and practice hub: a place to keep solved DSA problems organized by topic, data structure, company, and supporting interview resources.

A–Z DSA Concepts Reference

Letter	Concept	Category
0-9	0/1 Knapsack	Dynamic Programming
0-9	4 Queens	Backtracking
0-9	8 Queens	Backtracking
A	Array	Data Structure
A	A* Algorithm	Graph Algorithm
A	Adaptive and Non-Adaptive Searching Algorithms	Searching
A	Advanced Data Structures	Data Structures
A	Algorithm	Fundamentals
A	Angular Sweep	Computational Geometry
A	AVL Tree	Tree
B	B-Tree	Tree
B	Backtracking	Algorithmic Paradigm
B	Balanced Binary Tree	Tree
B	Bellman-Ford Algorithm	Graph Algorithm
B	Binary Search	Searching
B	Binary Tree	Tree
B	Binary Search Tree (BST)	Tree
B	Binary Heap	Heap
B	Bit Manipulation	Bitwise Techniques
B	Boyer-Moore Algorithm	String Algorithm
B	Booth's Multiplication Algorithm	Mathematical Algorithm
B	Breadth-First Search (BFS)	Graph Traversal
B	Bucket Sort	Sorting
C	Circular Array	Array
C	Circular Linked List	Linked List
C	Circular Queue	Queue
C	Complete Binary Tree	Tree
C	Complexity Analysis	Analysis
C	Convex Hull	Computational Geometry
D	Depth-First Search (DFS)	Graph Traversal
D	Divide and Conquer	Algorithmic Paradigm
D	Dijkstra's Algorithm	Graph Algorithm
D	Doubly Linked List	Linked List
D	Dynamic Arrays	Array
D	Dynamic Programming	Algorithmic Paradigm
D	Dynamic Segment Tree	Tree
E	Exponential Search	Searching
E	Expression Trees	Tree
E	Extended Binary Trees	Tree
E	External Sorting	Sorting
F	Factorial of a Number	Mathematics
F	Fenwick Tree (BIT)	Tree
F	Floyd-Warshall Algorithm	Graph Algorithm
F	Fibonacci Heap	Heap
F	Full Binary Tree	Tree
G	Graphs	Data Structure
G	Greedy Algorithms	Algorithmic Paradigm
H	Hash Table	Hashing
H	Hash Map	Hashing
H	Huffman Coding	Greedy Algorithm
I	Inorder Traversal	Tree Traversal
I	Insertion Sort	Sorting
I	Interval Tree	Tree
I	Inversion Count	Array/Divide & Conquer
I	Invert Binary Tree	Tree
J	Jump Search	Searching
J	Jagged Arrays	Array
J	JSON Array	Data Format
K	Kadane's Algorithm	Dynamic Programming
K	KMP Algorithm	String Algorithm
K	Kruskal's Algorithm	Graph Algorithm
L	Lazy Propagation in Segment Tree	Tree
L	Linear Search	Searching
L	Linked List	Data Structure
L	Longest Common Subsequence (LCS)	Dynamic Programming
L	LRU Cache	Design/Data Structure
M	Merge Sort	Sorting
M	Matrix Chain Multiplication	Dynamic Programming
M	Minimum Spanning Tree (MST)	Graph Algorithm
M	Multidimensional Arrays	Array
N	N-ary Tree	Tree
N	N-Queens	Backtracking
N	Network Flow	Graph Algorithm
O	Order Statistics	Selection Algorithm
O	Overlapping Subproblems	Dynamic Programming
P	Postorder Traversal	Tree Traversal
P	Preorder Traversal	Tree Traversal
P	Priority Queue	Data Structure
P	Prim's Algorithm	Graph Algorithm
Q	Quick Sort	Sorting
Q	Queue	Data Structure
Q	Quadtree	Tree
R	Rabin-Karp Algorithm	String Algorithm
R	Radix Sort	Sorting
R	Recursion	Fundamental Technique
R	Red-Black Tree	Tree
R	Rubik's Cube Algorithm	Puzzle Algorithm
S	Segment Tree	Tree
S	Selection Sort	Sorting
S	Shortest Path Algorithms	Graph Algorithm
S	Singly Linked List	Linked List
S	Sorting Algorithms	Algorithms
S	Sparse Arrays	Array
S	Stack	Data Structure
S	Strings	Data Structure
S	Subset Sum Problem	Dynamic Programming
S	Sudoku Problem	Backtracking
T	Topological Sorting	Graph Algorithm
T	Tree Traversal	Tree
T	Trie	Tree
T	Two Pointer Algorithm	Array Technique
T	Two-Dimensional Segment Tree	Tree
T	Types of Asymptotic Notations	Complexity Analysis
U	Union-Find (Disjoint Set Union - DSU)	Data Structure
V	Vertex Cover	Graph Algorithm
W	Warshall's Algorithm	Graph Algorithm
W	Weighted Graphs	Graph Theory
X	XOR Linked List	Linked List
X	XOR Trees	Tree
Y	Y-Shaped Pattern	Pattern Problem
Z	Z-Algorithm	String Algorithm
Z	Zigzag Traversal	Tree Traversal

What This Repository Contains

• Topic-wise DSA solutions covering algorithms, data structures, strings, and company-wise practice.
• Java-first implementations, with a few supporting files in other languages where useful.
• Notes and learning material for interview preparation.
• SQL, AI/ML, data science, and placement resources that support broader technical preparation.
• Automated LeetCode solution folders for generated or imported problem entries.

Repository Structure

dsahub/
|-- DSA/
|   |-- Algorithms/          # Algorithmic patterns and techniques
|   |-- Data Structures/     # Core data-structure implementations and problems
|   |-- Company Wise/        # Company-tagged coding questions and notes
|   `-- String/              # String-focused problems and techniques
|-- LeetcodeAutomated/       # LeetCode problem folders with solution files
|-- Interview/               # CS notes, interview tips, SQL, and system-design material
|-- AI ML/                   # Data science, machine learning, and deep learning resources
|-- Resources/               # DSA sheets, guides, PDFs, and extra reference material
|-- image/                   # Images used by notes and documentation
|-- CONTRIBUTING.md          # Contribution guidelines
|-- LICENSE.md               # License information
`-- README.md                # Repository overview

How To Use

1. Pick a topic from DSA/Algorithms/ or DSA/Data Structures/.
2. Read the related notes if a folder contains a README.md, ReadME.MD, or Learn.MD.
3. Open the solution file and trace the approach.
4. Re-solve the problem independently before checking the saved implementation.
5. Use DSA/Company Wise/ when preparing for a specific company.

Running Solutions

Most solution files are standalone Java programs or LeetCode-style classes. For standalone Java files:

javac FileName.java
java FileName

Some files are intended for online judges and may need the platform-provided method signature or driver code.

Supporting Material

Beyond solved DSA problems, this repository also includes:

• Interview/ for CS fundamentals, SQL, system design, HR questions, and interview notes.
• Resources/ for DSA sheets, guides, and revision material.
• AI ML/ for data science, machine learning, and deep learning references.
• LeetcodeAutomated/ for LeetCode problem folders generated or saved during practice.

Contributing

Contributions are welcome when they keep the repository organized and useful.

Before contributing:

• Follow the existing folder structure.
• Place solutions in the most relevant topic or company folder.
• Use clear file names that describe the problem.
• Add short notes when an approach is non-obvious.
• Read CONTRIBUTING.md before opening a pull request.

License

This repository is licensed under the terms of LICENSE.md.

Maintained by Shaikh Rumman Fardeen GitHub: @srummanf rummanfardeen4567@gmail.com

Project Links Live Demo Source Code MIT License | Contributing