Discover simple and fun DSA project ideas to help you practice and improve your skills. Perfect for beginners or anyone looking to learn!
Hey! Looking to improve your Data Structures and Algorithms (DSA) skills? Doing projects is a great way to practice and really see how DSA works in real life. These project ideas are easy to follow and perfect for beginners or anyone wanting to try something new. Ready to pick a fun project? Let’s dive in!
DSA Project Ideas PDF
Importance of DSA Projects
Check out the importance of DSA projects:-
| Benefit | Description |
|---|---|
| Hands-on Practice | Helps you learn DSA by actually using it. |
| Better Problem Solving | Makes you better at solving tricky problems. |
| Interview Help | Prepares you for coding interviews. |
| Boosts Confidence | Builds your confidence in coding. |
| Strong Portfolio | Shows employers you can apply DSA. |
| Deep Understanding | Helps you really understand DSA concepts. |
| Sharpens Thinking | Improves logical thinking and reasoning skills. |
These points make it clear why DSA projects are important!
DSA Project Ideas
Check out DSA project ideas:-
Sorting Algorithms
Bubble Sort Visualizer
Objective: Show how Bubble Sort works.
Key Concepts: Bubble Sort.
Skill Required: Basic sorting knowledge.
Learning Outcome: Understand Bubble Sort visually.
Merge Sort Implementation
Objective: Build Merge Sort algorithm.
Key Concepts: Merge Sort.
Skill Required: Sorting algorithm basics.
Learning Outcome: Learn how to sort using Merge Sort.
Quick Sort Performance Analyzer
Objective: Compare Quick Sort performance with different data.
Key Concepts: Quick Sort.
Skill Required: Basic understanding of Quick Sort.
Learning Outcome: Analyze Quick Sort speed and efficiency.
Heap Sort Simulator
Objective: Visualize Heap Sort process.
Key Concepts: Heap Sort.
Skill Required: Knowledge of Heap Sort.
Learning Outcome: See Heap Sort in action.
Counting Sort for Large Data
Objective: Implement Counting Sort for big data sets.
Key Concepts: Counting Sort.
Skill Required: Understanding Counting Sort.
Learning Outcome: Efficient sorting for large lists.
Radix Sort Implementation
Objective: Build Radix Sort algorithm.
Key Concepts: Radix Sort.
Skill Required: Basic Radix Sort knowledge.
Learning Outcome: Learn sorting with Radix Sort.
Bucket Sort for Random Data
Objective: Use Bucket Sort for random numbers.
Key Concepts: Bucket Sort.
Skill Required: Understanding Bucket Sort.
Learning Outcome: Sort random data using Buckets.
Gnome Sort Implementation
Objective: Create Gnome Sort algorithm.
Key Concepts: Gnome Sort.
Skill Required: Basic Gnome Sort knowledge.
Learning Outcome: Implement and test Gnome Sort.
Tim Sort Analysis
Objective: Analyze Tim Sort performance.
Key Concepts: Tim Sort.
Skill Required: Understanding Tim Sort.
Learning Outcome: Evaluate Tim Sort efficiency.
Shell Sort Demonstration
Objective: Show how Shell Sort works.
Key Concepts: Shell Sort.
Skill Required: Basic Shell Sort knowledge.
Learning Outcome: Visualize Shell Sort process.
Graph Algorithms
Shortest Path Finder
Objective: Find the shortest path between nodes.
Key Concepts: Dijkstra’s algorithm.
Skill Required: Basic graph knowledge.
Learning Outcome: Find shortest routes.
Graph Coloring Solver
Objective: Assign colors to graph nodes.
Key Concepts: Graph coloring.
Skill Required: Understanding graph coloring.
Learning Outcome: Solve color assignment problems.
Network Flow Analyzer
Objective: Analyze flow in a network.
Key Concepts: Ford-Fulkerson algorithm.
Skill Required: Basic network flow knowledge.
Learning Outcome: Manage network flow.
Minimum Spanning Tree Builder
Objective: Build a minimum spanning tree.
Key Concepts: Kruskal’s and Prim’s algorithms.
Skill Required: Knowledge of MST algorithms.
Learning Outcome: Construct spanning trees.
Topological Sort Generator
Objective: Order nodes in a directed graph.
Key Concepts: Topological sorting.
Skill Required: Basic graph traversal.
Learning Outcome: Order tasks in sequence.
Eulerian Path Finder
Objective: Find an Eulerian path in a graph.
Key Concepts: Eulerian path.
Skill Required: Understanding of Eulerian paths.
Learning Outcome: Solve Eulerian path problems.
Hamiltonian Path Solver
Objective: Find a Hamiltonian path in a graph.
Key Concepts: Hamiltonian path.
Skill Required: Knowledge of Hamiltonian paths.
Learning Outcome: Identify Hamiltonian paths.
Graph Traversal Visualizer
Objective: Visualize BFS and DFS traversals.
Key Concepts: BFS, DFS.
Skill Required: Understanding traversal algorithms.
Learning Outcome: See graph traversals.
Bipartite Graph Checker
Objective: Check if a graph is bipartite.
Key Concepts: Bipartite graphs.
Skill Required: Basic graph theory.
Learning Outcome: Verify graph bipartiteness.
Strongly Connected Components
Objective: Find strongly connected components in a graph.
Key Concepts: Tarjan’s algorithm.
Skill Required: Understanding of SCCs.
Learning Outcome: Identify strong connectivity in graphs.
Dynamic Programming
Fibonacci Sequence Calculator
Objective: Compute Fibonacci numbers using dynamic programming.
Key Concepts: Dynamic programming.
Skill Required: Basic DP understanding.
Learning Outcome: Efficient Fibonacci calculation.
Knapsack Problem Solver
Objective: Solve the knapsack optimization problem.
Key Concepts: Dynamic programming.
Skill Required: Knowledge of optimization.
Learning Outcome: Optimize item selection.
Coin Change Calculator
Objective: Find minimum coins needed for a given amount.
Key Concepts: Dynamic programming.
Skill Required: Basic DP knowledge.
Learning Outcome: Solve currency problems.
Longest Increasing Subsequence
Objective: Find the longest increasing subsequence.
Key Concepts: Dynamic programming.
Skill Required: Understanding subsequences.
Learning Outcome: Identify increasing subsequences.
Minimum Cost Path Finder
Objective: Find the minimum cost path in a grid.
Key Concepts: Dynamic programming.
Skill Required: Grid-based problem solving.
Learning Outcome: Solve pathfinding issues.
Matrix Chain Multiplication
Objective: Optimize matrix multiplication order.
Key Concepts: Dynamic programming.
Skill Required: Matrix operations knowledge.
Learning Outcome: Efficient matrix multiplication.
Palindrome Partitioning
Objective: Partition a string into palindromes.
Key Concepts: Dynamic programming.
Skill Required: String partitioning basics.
Learning Outcome: Analyze palindromic substrings.
Rod Cutting Problem
Objective: Maximize profit from cutting and selling rods.
Key Concepts: Dynamic programming.
Skill Required: Optimization and profit calculation.
Learning Outcome: Optimize rod cutting.
Longest Common Subsequence
Objective: Find the longest common subsequence between two strings.
Key Concepts: Dynamic programming.
Skill Required: Sequence comparison.
Learning Outcome: Identify common patterns.
Optimal Binary Search Tree
Objective: Build an optimal binary search tree.
Key Concepts: Dynamic programming, binary search trees.
Skill Required: Binary search tree knowledge.
Learning Outcome: Create efficient search trees.
Tree-based Projects
File Directory Structure
Objective: Create a file directory system using trees.
Key Concepts: Tree structures.
Skill Required: Basic file management.
Learning Outcome: Organize hierarchical data.
Family Tree Visualizer
Objective: Visualize a family tree.
Key Concepts: Tree traversal, visualization.
Skill Required: Tree structures and visualization.
Learning Outcome: Display family relationships.
Spell Checker
Objective: Implement a spell checker with a trie.
Key Concepts: Trie data structure.
Skill Required: Trie operations.
Learning Outcome: Process text for spelling errors.
Binary Search Tree Implementation
Objective: Build a binary search tree.
Key Concepts: Binary search tree operations.
Skill Required: Basic tree structures.
Learning Outcome: Manage search trees.
AVL Tree-based Dictionary
Objective: Implement a dictionary with an AVL tree.
Key Concepts: AVL trees, balanced trees.
Skill Required: Balanced tree knowledge.
Learning Outcome: Use self-balancing trees.
Huffman Coding-based File Compressor
Objective: Compress files using Huffman coding.
Key Concepts: Huffman coding, binary trees.
Skill Required: Compression algorithms.
Learning Outcome: Efficient data compression.
Segment Tree for Range Queries
Objective: Handle range queries with a segment tree.
Key Concepts: Segment trees.
Skill Required: Range query operations.
Learning Outcome: Efficient query handling.
Expression Tree Evaluator
Objective: Evaluate expressions using a binary tree.
Key Concepts: Expression trees.
Skill Required: Expression evaluation techniques.
Learning Outcome: Process mathematical expressions.
Decision Tree Classifier
Objective: Build a decision tree for classification.
Key Concepts: Decision trees.
Skill Required: Classification and tree building.
Learning Outcome: Classify data using trees.
Red-Black Tree Implementation
Objective: Implement a red-black tree for balanced search.
Key Concepts: Red-black trees.
Skill Required: Knowledge of balanced trees.
Learning Outcome: Manage balanced search trees.
String Manipulation
Palindrome Checker
Objective: Check if a string is a palindrome.
Key Concepts: String reversal.
Skill Required: Basic string operations.
Learning Outcome: Detect palindromes.
Anagram Finder
Objective: Find anagrams in a list of words.
Key Concepts: String sorting, comparison.
Skill Required: Anagram detection.
Learning Outcome: Identify anagrams.
String Compression Tool
Objective: Compress a string using a basic algorithm.
Key Concepts: String compression.
Skill Required: Compression techniques.
Learning Outcome: Reduce string size.
Text Editor with Find and Replace
Objective: Implement find and replace functionality.
Key Concepts: String searching.
Skill Required: Basic text manipulation.
Learning Outcome: Edit text efficiently.
Substring Search in a Text
Objective: Find all occurrences of a substring.
Key Concepts: String searching algorithms.
Skill Required: Searching techniques.
Learning Outcome: Efficient substring searching.
Longest Common Prefix Finder
Objective: Find the longest common prefix in strings.
Key Concepts: Prefix trees.
Skill Required: Trie operations.
Learning Outcome: Find common prefixes.
Levenshtein Distance Calculator
Objective: Measure the difference between two strings.
Key Concepts: Edit distance algorithm.
Skill Required: Distance calculation.
Learning Outcome: Compare string similarity.
String Permutations Generator
Objective: Generate all permutations of a string.
Key Concepts: Permutation generation.
Skill Required: String manipulation.
Learning Outcome: Explore string permutations.
Text Formatter
Objective: Format text to fit a specific width.
Key Concepts: Text wrapping.
Skill Required: Formatting techniques.
Learning Outcome: Format text for display.
Substring with Concatenation of All Words
Objective: Find substrings that are concatenations of given words.
Key Concepts: Sliding window technique.
Skill Required: String manipulation.
Learning Outcome: Detect concatenated substrings.
Graph-based Applications
Social Network Analysis
Objective: Analyze connections in a social network.
Key Concepts: Community detection.
Skill Required: Network analysis.
Learning Outcome: Understand social connections.
Route Optimization for Delivery
Objective: Optimize delivery routes.
Key Concepts: Routing algorithms.
Skill Required: Delivery optimization.
Learning Outcome: Improve route efficiency.
Road Traffic Simulation
Objective: Simulate traffic patterns.
Key Concepts: Traffic modeling.
Skill Required: Traffic simulation techniques.
Learning Outcome: Analyze traffic flow.
Resource Allocation in Project Management
Objective: Optimize resource use in projects.
Key Concepts: Project scheduling.
Skill Required: Resource management.
Learning Outcome: Efficiently allocate resources.
Crime Network Analysis
Objective: Analyze crime patterns in a network.
Key Concepts: Crime network analysis.
Skill Required: Network analysis.
Learning Outcome: Detect crime patterns.
Interactive Map with Points of Interest
Objective: Create a map with locations marked.
Key Concepts: Map navigation.
Skill Required: Mapping and UI design.
Learning Outcome: Create interactive maps.
Fraud Detection System
Objective: Detect fraudulent transactions.
Key Concepts: Fraud detection.
Skill Required: Financial fraud analysis.
Learning Outcome: Identify fraudulent activity.
Course Prerequisite Tracker
Objective: Track course prerequisites in an academic program.
Key Concepts: Prerequisite tracking.
Skill Required: Academic program management.
Learning Outcome: Manage course prerequisites.
Travel Itinerary Planner
Objective: Plan and optimize travel routes.
Key Concepts: Itinerary optimization.
Skill Required: Travel planning.
Learning Outcome: Optimize travel plans.
Sports League Scheduling
Objective: Schedule sports league matches.
Key Concepts: Scheduling algorithms.
Skill Required: League management.
Learning Outcome: Manage sports schedules.
Graph Traversal
Network Connectivity Checker
Objective: Check if nodes are connected.
Key Concepts: BFS, DFS.
Skill Required: Graph traversal basics.
Learning Outcome: Verify network connectivity.
Web Crawler
Objective: Crawl and index web pages.
Key Concepts: Web scraping.
Skill Required: Web crawling techniques.
Learning Outcome: Index web content.
Peer-to-Peer Network
Objective: Create a network with peer nodes.
Key Concepts: Peer communication.
Skill Required: P2P networking.
Learning Outcome: Implement peer-to-peer systems.
Recommendation System Based on User Behavior
Objective: Recommend items based on user actions.
Key Concepts: Recommendation algorithms.
Skill Required: User behavior analysis.
Learning Outcome: Build recommendation systems.
Graph Coloring Problem
Objective: Color graph nodes with minimal colors.
Key Concepts: Graph coloring.
Skill Required: Coloring algorithms.
Learning Outcome: Solve coloring problems.
Network Analysis for Social Media
Objective: Analyze social media connections.
Key Concepts: Social network analysis.
Skill Required: Social network data handling.
Learning Outcome: Understand social media networks.
Optimal Path Finding in a Grid
Objective: Find the best path in a grid.
Key Concepts: Pathfinding algorithms.
Skill Required: Grid traversal techniques.
Learning Outcome: Navigate grids efficiently.
Real-time Navigation System
Objective: Develop a navigation system for real-time use.
Key Concepts: Navigation algorithms.
Skill Required: Real-time data handling.
Learning Outcome: Build navigation systems.
Graph-based Puzzle Solver
Objective: Solve puzzles using graph algorithms.
Key Concepts: Graph-based puzzle solving.
Skill Required: Puzzle-solving techniques.
Learning Outcome: Solve puzzles with graphs.
Campus Navigation System
Objective: Create a campus navigation system.
Key Concepts: Campus mapping.
Skill Required: Navigation system design.
Learning Outcome: Manage campus navigation.
Advanced Data Structures
Segment Tree for Range Queries
Objective: Implement a segment tree for range queries.
Key Concepts: Segment trees.
Skill Required: Segment tree basics.
Learning Outcome: Handle range queries.
Fenwick Tree for Cumulative Frequency
Objective: Use a Fenwick tree for frequency calculations.
Key Concepts: Fenwick tree.
Skill Required: Basic Fenwick tree operations.
Learning Outcome: Manage cumulative frequency.
Trie-based Autocomplete System
Objective: Build an autocomplete feature with a trie.
Key Concepts: Tries.
Skill Required: Autocomplete algorithms.
Learning Outcome: Implement autocomplete.
Splay Tree for Self-Balancing
Objective: Implement a splay tree for self-balancing.
Key Concepts: Splay trees.
Skill Required: Splay tree knowledge.
Learning Outcome: Use self-balancing trees.
Red-Black Tree for Balanced Search
Objective: Build a red-black tree for balanced searches.
Key Concepts: Red-black trees.
Skill Required: Balanced tree operations.
Learning Outcome: Manage balanced search trees.
AVL Tree for Efficient Lookups
Objective: Implement an AVL tree for efficient lookups.
Key Concepts: AVL trees.
Skill Required: AVL tree operations.
Learning Outcome: Efficient searching with AVL trees.
B-Tree for Database Indexing
Objective: Use a B-tree for indexing data in databases.
Key Concepts: B-trees.
Skill Required: Database indexing.
Learning Outcome: Implement indexing with B-trees.
K-D Tree for Multi-Dimensional Search
Objective: Build a K-D tree for multi-dimensional data.
Key Concepts: K-D trees.
Skill Required: Multi-dimensional searching.
Learning Outcome: Efficient multi-dimensional searches.
Skip List for Fast Search
Objective: Implement a skip list for quick searches.
Key Concepts: Skip lists.
Skill Required: Skip list operations.
Learning Outcome: Fast search with skip lists.
Suffix Tree for Substring Searches
Objective: Build a suffix tree for efficient substring searches.
Key Concepts: Suffix trees.
Skill Required: Substring search algorithms.
Learning Outcome: Implement substring searches.
Sorting-based Applications
Leaderboard System
Objective: Create a system to manage and sort leaderboards.
Key Concepts: Sorting algorithms.
Skill Required: Leaderboard management.
Learning Outcome: Manage and sort leaderboards.
Event Scheduling System
Objective: Sort and schedule events.
Key Concepts: Sorting and scheduling.
Skill Required: Event management.
Learning Outcome: Organize events efficiently.
Student Grade Management
Objective: Sort and manage student grades.
Key Concepts: Sorting and data management.
Skill Required: Grade management.
Learning Outcome: Handle student grades.
Task Prioritization Tool
Objective: Prioritize tasks based on importance.
Key Concepts: Task sorting.
Skill Required: Task management.
Learning Outcome: Efficient task prioritization.
Order Processing System
Objective: Sort and process orders.
Key Concepts: Order management.
Skill Required: Order sorting.
Learning Outcome: Manage and process orders.
Book Catalog System
Objective: Create and sort a catalog of books.
Key Concepts: Catalog management.
Skill Required: Book sorting.
Learning Outcome: Organize book catalogs.
Sales Data Analysis
Objective: Analyze and sort sales data.
Key Concepts: Data analysis and sorting.
Skill Required: Sales data management.
Learning Outcome: Analyze sales trends.
Customer Feedback Sorting
Objective: Sort and analyze customer feedback.
Key Concepts: Feedback analysis.
Skill Required: Feedback sorting.
Learning Outcome: Analyze customer feedback.
Inventory Management System
Objective: Sort and manage inventory items.
Key Concepts: Inventory management.
Skill Required: Item sorting and tracking.
Learning Outcome: Handle inventory items.
Fitness Tracker Data Analysis
Objective: Sort and analyze fitness data.
Key Concepts: Data analysis.
Skill Required: Fitness data management.
Learning Outcome: Analyze fitness trends.
Search Algorithms
Linear Search Algorithm
Objective: Implement a simple linear search.
Key Concepts: Linear search.
Skill Required: Basic search knowledge.
Learning Outcome: Understand linear search.
Binary Search Algorithm
Objective: Implement binary search for sorted data.
Key Concepts: Binary search.
Skill Required: Knowledge of sorted arrays.
Learning Outcome: Efficiently search sorted lists.
Jump Search Algorithm
Objective: Implement jump search for sorted data.
Key Concepts: Jump search.
Skill Required: Understanding jump search.
Learning Outcome: Search efficiently in sorted lists.
Interpolation Search Algorithm
Objective: Implement interpolation search for sorted data.
Key Concepts: Interpolation search.
Skill Required: Knowledge of interpolation techniques.
Learning Outcome: Advanced search in sorted data.
Exponential Search Algorithm
Objective: Implement exponential search.
Key Concepts: Exponential search.
Skill Required: Search algorithm knowledge.
Learning Outcome: Efficient searching in large lists.
Ternary Search Algorithm
Objective: Implement ternary search.
Key Concepts: Ternary search.
Skill Required: Understanding ternary search.
Learning Outcome: Search efficiently in sorted data.
Sublist Search Algorithm
Objective: Search for a sublist within a list.
Key Concepts: Sublist search.
Skill Required: Pattern matching.
Learning Outcome: Find sublists efficiently.
Pattern Matching with Knuth-Morris-Pratt
Objective: Implement KMP pattern matching.
Key Concepts: KMP algorithm.
Skill Required: Pattern matching.
Learning Outcome: Efficient pattern matching.
A Search Algorithm
Objective: Implement the A* search algorithm for pathfinding.
Key Concepts: A* search.
Skill Required: Pathfinding techniques.
Learning Outcome: Find optimal paths.
Depth-First Search (DFS)
Objective: Implement DFS for graph traversal.
Key Concepts: DFS.
Skill Required: Graph traversal basics.
Learning Outcome: Traverse graphs using DFS.
Backtracking Algorithms
N-Queens Problem
Objective: Solve the N-Queens puzzle.
Key Concepts: Backtracking.
Skill Required: Basic backtracking.
Learning Outcome: Place queens on a chessboard.
Sudoku Solver
Objective: Solve Sudoku puzzles using backtracking.
Key Concepts: Backtracking.
Skill Required: Sudoku rules.
Learning Outcome: Solve Sudoku puzzles.
Rat in a Maze
Objective: Find a path for a rat in a maze.
Key Concepts: Maze solving.
Skill Required: Maze traversal.
Learning Outcome: Pathfinding in mazes.
Knapsack Problem (Backtracking)
Objective: Solve the knapsack problem using backtracking.
Key Concepts: Backtracking.
Skill Required: Knapsack problem basics.
Learning Outcome: Optimize item selection.
Word Search Puzzle
Objective: Find words in a grid.
Key Concepts: Word search.
Skill Required: Puzzle-solving techniques.
Learning Outcome: Solve word search puzzles.
Combination Sum Problem
Objective: Find combinations that sum to a target.
Key Concepts: Backtracking.
Skill Required: Combination finding.
Learning Outcome: Find combinations of numbers.
Permutations of a String
Objective: Generate all permutations of a string.
Key Concepts: Backtracking.
Skill Required: Permutation generation.
Learning Outcome: Generate permutations.
Hamiltonian Path Finder
Objective: Find a Hamiltonian path in a graph.
Key Concepts: Backtracking.
Skill Required: Graph traversal.
Learning Outcome: Find Hamiltonian paths.
Subset Sum Problem
Objective: Find subsets that sum to a given value.
Key Concepts: Backtracking.
Skill Required: Subset finding.
Learning Outcome: Find subsets with specific sums.
Subset Generation
Objective: Generate all subsets of a set.
Key Concepts: Backtracking.
Skill Required: Subset generation.
Learning Outcome: Generate subsets of a set.
Dsa Project Ideas with Source Code
Check out DSA project ideas with source code:-
Linked List Implementation
Project Idea: Implement a basic singly linked list with operations such as insertion, deletion, and traversal.
Source Code
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def append(self, data):
if not self.head:
self.head = Node(data)
return
last = self.head
while last.next:
last = last.next
last.next = Node(data)
def delete(self, key):
temp = self.head
if temp is not None:
if temp.data == key:
self.head = temp.next
temp = None
return
prev = None
while temp is not None:
if temp.data == key:
break
prev = temp
temp = temp.next
if temp == None:
return
prev.next = temp.next
temp = None
def display(self):
temp = self.head
while temp:
print(temp.data, end=" -> ")
temp = temp.next
print("None")Example usage
llist = LinkedList()
llist.append(1)
llist.append(2)
llist.append(3)
llist.display() # Output: 1 -> 2 -> 3 -> None
llist.delete(2)
llist.display() # Output: 1 -> 3 -> None
Stack Using Linked List
Project Idea: Implement a stack using a singly linked list with operations like push, pop, and peek.
Source Code
class Node:
def __init__(self, data):
self.data = data
self.next = None
class Stack:
def __init__(self):
self.top = None
def push(self, data):
new_node = Node(data)
new_node.next = self.top
self.top = new_node
def pop(self):
if self.is_empty():
raise IndexError("Pop from an empty stack")
popped_data = self.top.data
self.top = self.top.next
return popped_data
def peek(self):
if self.is_empty():
raise IndexError("Peek from an empty stack")
return self.top.data
def is_empty(self):
return self.top is NoneExample usage
stack = Stack()
stack.push(10)
stack.push(20)
print(stack.peek()) # Output: 20
print(stack.pop()) # Output: 20
print(stack.peek()) # Output: 10
Binary Search Tree (BST)
Project Idea: Implement a Binary Search Tree with operations like insertion, searching, and in-order traversal.
Source Code
class TreeNode:
def __init__(self, key):
self.left = None
self.right = None
self.value = key
class BinarySearchTree:
def __init__(self):
self.root = None
def insert(self, key):
if self.root is None:
self.root = TreeNode(key)
else:
self._insert(self.root, key)
def _insert(self, node, key):
if key < node.value:
if node.left is None:
node.left = TreeNode(key)
else:
self._insert(node.left, key)
else:
if node.right is None:
node.right = TreeNode(key)
else:
self._insert(node.right, key)
def search(self, key):
return self._search(self.root, key)
def _search(self, node, key):
if node is None or node.value == key:
return node
if key < node.value:
return self._search(node.left, key)
return self._search(node.right, key)
def inorder_traversal(self):
res = []
self._inorder(self.root, res)
return res
def _inorder(self, node, res):
if node:
self._inorder(node.left, res)
res.append(node.value)
self._inorder(node.right, res)Example usage
bst = BinarySearchTree()
bst.insert(10)
bst.insert(5)
bst.insert(15)
print(bst.inorder_traversal()) # Output: [5, 10, 15]
print(bst.search(10)) # Output: <__main__.TreeNode object at …>
print(bst.search(20)) # Output: None
These projects cover fundamental data structures and their applications, giving you a solid foundation in DSA. Feel free to expand or modify them as needed!
Data structures project ideas in C++
Check out data structures project ideas in C++:-
| Project | Description | Data Structures | Operations |
|---|---|---|---|
| Simple To-Do List | Manage tasks | Array | Add, remove, display tasks |
| Stack Implementation | Create a stack with push and pop | Array or Linked List | Push, pop, peek |
| Queue Implementation | Implement a queue with enqueue and dequeue | Array or Linked List | Enqueue, dequeue, peek |
| Basic Linked List Ops | Insert, delete, and traverse a list | Singly Linked List | Insert, delete, traverse |
| Simple Phone Book | Manage contacts | Array of Structs | Add, search, display contacts |
| Tic-Tac-Toe Game | Console-based Tic-Tac-Toe | 2D Array | Place mark, check win/draw, display board |
| Palindrome Checker | Check if a string is a palindrome | Stack (optional), Array | Reverse, compare |
| Student Grade Book | Manage student names and grades | Array of Structs | Add/update grade, display students and grades |
| Basic Array Operations | Implement basic array operations | Array | Insert, delete, display elements |
| Simple Number List | Manage a list of numbers | Vector | Add, remove, find max/min |
These projects should help you get a good grasp of fundamental data structures with simple implementations and operations.
DSA Project Ideas in Python
Check out DSA project ideas in Python:-
| Project | Description | Data Structures | Operations |
|---|---|---|---|
| Simple Contact List | Manage contacts with names and phone numbers | List of Dictionaries | Add, delete, search contacts, display all contacts |
| Basic Stack Implementation | Implement a stack with push, pop, and peek | List | Push, pop, peek at top item |
| Queue Implementation | Implement a queue with enqueue and dequeue operations | List | Enqueue, dequeue, peek at front item |
| Simple Linked List | Create a singly linked list with basic operations | Class-based Nodes | Insert, delete, traverse and display list |
| Basic Binary Search Tree (BST) | Implement a BST for storing and retrieving data | Class-based Nodes | Insert, search, in-order traversal |
| To-Do List Application | Manage a list of tasks with add, remove, and view options | List | Add task, remove task, display all tasks |
| Simple Graph Representation | Represent a graph using an adjacency list or matrix | Dictionary of Lists, 2D List | Add vertices/edges, display the graph |
| Basic Sorting Algorithms | Implement and compare sorting algorithms | List | Bubble Sort, Selection Sort, Insertion Sort, compare sorting times |
| Simple Pathfinding Algorithm | Implement a basic pathfinding algorithm like BFS | Queue, Graph Representation (Adj. List) | Find shortest path, display the path |
| Palindrome Checker | Check if a string is a palindrome | Stack (optional), List | Reverse string, compare with original |
These projects cover a range of fundamental concepts in data structures and algorithms, and are well-suited for gaining practical experience in Python.
DSA Project Ideas in C
Check out DSA project ideas in C:-
| Project | Description | Data Structures | Operations |
|---|---|---|---|
| Simple Contact Manager | Manage contacts with names and phone numbers | Array of Structs | Add, delete, search contacts, display all contacts |
| Basic Stack Implementation | Implement a stack with push, pop, and peek operations | Array or Linked List | Push, pop, peek at top element |
| Queue Implementation | Create a queue with enqueue and dequeue operations | Array or Linked List | Enqueue, dequeue, peek at front element |
| Simple Linked List | Create a singly linked list with basic operations | Structs for Nodes | Insert, delete, traverse and display list |
| Basic Binary Search Tree (BST) | Implement a BST for storing and retrieving values | Structs for Nodes | Insert value, search value, in-order traversal |
| To-Do List Application | Manage tasks with add, remove, and display options | Array of Strings or Structs | Add task, remove task, display all tasks |
| Simple Graph Representation | Represent a graph using an adjacency list or matrix | Array of Lists, 2D Array | Add vertices/edges, display the graph |
| Basic Sorting Algorithms | Implement and compare common sorting algorithms | Array | Bubble Sort, Selection Sort, Insertion Sort, compare sorting times |
| Simple Pathfinding Algorithm | Implement a basic pathfinding algorithm like BFS | Queue, Adjacency List or Matrix | Find shortest path, display the path |
| Palindrome Checker | Check if a string is a palindrome | Array | Reverse string, compare with original |
Conclusion
Jumping into Data Structures and Algorithms (DSA) projects is a fantastic way to grasp these essential concepts. By actively working on projects, you can explore sorting techniques such as quicksort, which efficiently divides and conquers large datasets, and mergesort, known for its stable and predictable performance.
Building and optimizing data structures like balanced trees—such as AVL or Red-Black trees—helps you understand how to maintain order and balance for fast retrieval and updates. Implementing priority queues, whether through binary heaps or Fibonacci heaps, teaches you how to efficiently manage and access elements based on priority.
These projects transform abstract concepts into practical experiences, allowing you to experiment with real-world scenarios and performance trade-offs. This hands-on approach not only solidifies your theoretical knowledge but also makes the learning process both enjoyable and rewarding.
