If you are a full-stack software developer, learning Data Structures and Algorithms is essential for organizing data and solving problems efficiently. Not only full-stack developers but anyone working in the programming domain needs to have basic concepts of data structures and algorithms.
After training 30,000+ students in the last five years, we saw that theoretical knowledge isn’t enough for students to understand data structures and algorithms (DSA) concepts. Practical knowledge improves their problem-solving skills and helps them better understand the concepts.
But what are the best DSA projects to work on? We are here to share a few data structure project ideas you can start with.
Codegnan’s resources and courses:
- Data structures and algorithms training course in Vijayawada (Course fee: ₹30,000; Duration: 3 Months)
- Data structure course syllabus
- Online data analytics course
Contact us now for placement assistance and an additional offer on our course fees.
Data Structure project for beginners with source code
1. Creating a To-do list
Difficulty level: Easy
This project is suitable for beginners in Data Structure and Algorithms. You can mainly learn the implementation of CRUD operations (Read-Create-Update-Delete) into real-life projects, along with knowledge of different data structures and FIFO or LIFO methods. This To-Do list application will allow users to add tasks, update them as completed or incomplete, and delete them when done.
Learning outcomes:
- Knowledge of how to store and manage different tasks using arrays and lists
- Implement algorithms to search and sort tasks based on specific criteria like priority or due dates
- Implementation of linked list data structure for dynamically managing tasks
- Understanding the efficiency of different operations like adding, removing, and searching for time-complexity analysis
What it takes to execute this project:
- Define a data structure to store tasks, e.g., a linked list or array list.
- Implement functions to add, remove, and mark tasks as complete.
- Use a sorting algorithm like merge or quick sort algorithms to sort tasks based on priority or due date.
- Implement a search function, e.g., binary search or linear search, to find specific tasks.
- Consider using a hash table or tree for efficient task lookup and organization.
- Implement persistence by saving and loading tasks from a file or database.
- Utilize algorithms for task scheduling and prioritization, such as greedy or dynamic programming algorithms.
- Provide a user interface for interacting with the to-do list functionality.
Real-world application:
- Personal use: This To-do list application can help you manage daily activities, send reminders for deadlines and important dates, and stay organised
- Project management: Project managers can prepare a To-Do list for the team, working collaboratively to assign tasks and track their progress
- Remote workers management: People working remotes fail to manage time, and this application will help them prioritize tasks, set deadlines, allocate time for different activities, and improve time management skills
2. Building a Phonebook
Difficulty level: Easy
Building a phone book is another beginner-friendly project in which you can use an array data structure to store people’s information, such as their names and phone numbers. This application will help you add, view, edit, and delete contacts and also provide functionality for searching contacts based on various criteria, like names.
Learning outcomes:
- Using arrays and lists to store contact information
- Use of merge sort, bubble sort, or other sorting and searching algorithms to store contact details in an order
- You can implement a hashtable data structure for improving search operations efficiency
- Time complexity analysis of different operations performed on the application
What it takes to execute this project:
- Use a data structure like an array, linked list, or hash table to store contact records.
- Implement functions to add, remove, and search for contacts by name or number.
- Use sorting algorithms like merge sort or quick sort to arrange contacts alphabetically.
- Implement search algorithms like binary search or linear search for efficient contact lookup.
- Consider using a tree data structure like a binary search tree or trie for hierarchical organization.
- Implement persistence by saving and loading contacts from a file or database.
- Utilize hashing techniques for efficient storage and retrieval of contact records.
- Provide a user interface for adding, editing, deleting, and searching contacts in the phonebook.
Real-world application:
- Personal use: You can use this phonebook application to manage your contacts efficiently and access them whenever needed
- Business use: Businesses maintain a database of customer and client contacts, building communication and relationship management
- Hospitals and nursing homes: They can store emergency contacts and patient contacts to get connected effectively during emergency
3. Build a simple calculator
Difficulty level: Easy
This project aims to train beginners in developing a program with data structure and algorithms to perform basic mathematical operations like addition, subtraction, multiplication, and division. The system will accept user input for two numbers, perform required operation, and display you the result.
Learning outcomes:
- Fundamental concepts of data structures and algorithms related to mathematical operations, operator precedence, and user input handling
- Understanding stack data structures for handling operations with parenthesis
- Learn data type, variables, and basic mathematical operations
- Implement algorithms for input validation and error handling
- Designing user-friendly interfaces for input and output operations
What it takes to execute this project:
- Use a stack data structure to evaluate arithmetic expressions in prefix, infix, or postfix notation.
- Implement basic arithmetic operations (+, -, *, /) using functions or methods.
- Use a parser to tokenize and convert input expressions into a suitable internal representation.
- Implement algorithms for evaluating expressions, such as the shunting yard algorithm or recursive descent parsing.
- Handle operator precedence and associativity using rules or a precedence table.
- Consider using a tree data structure like an expression tree for more complex expressions.
- Implement error handling for invalid inputs or divisions by zero.
- Provide a user interface for entering expressions and displaying results.
Real-world application:
- Personal and business use: You can use this tool in various fields to solve simple calculations
- Government tax websites: These sites often allow users to calculate their taxes for the year, and integrating this tool can be helpful
- Banks and financial institutions: They can implement this tool to calculate the rate of interest on loans and various deposits
4. Students grade checker
Difficulty level: Easy
This project aims to develop a program that can calculate and display students’ grades based on their scores in various subjects. It can use a hash table to store and retrieve grades efficiently, and stores data as key-value pairs where students’ name or IDs is the ‘key’ and their grades are the ‘value.’ You can also implement functions to add or delete grades from the table.
Learning outcomes:
- Learn fundamental concepts of data structures and algorithms related to data manipulation and basic arithmetic calculations
- Use of hash tables to store students’ information and grades
- Understanding of searching and sorting algorithms to organize or search students’ data based on specific criteria like student ID, total score, etc.
- Knowledge of file input/output operations, error handling techniques, and designing user interface
What it takes to execute this project:
- Use a data structure like an array or linked list to store student records (name, grades).
- Implement functions to add, remove, and update student records.
- Use sorting algorithms (e.g., merge sort, quick sort) to sort student records by name or grade.
- Implement search algorithms (e.g., binary search, linear search) to find specific student records.
- Calculate and store aggregate statistics like class average and highest/lowest grades.
- Consider using a hash table or tree for efficient student lookup.
- Implement grade calculation algorithms based on course policies (e.g., weighted averages).
- Provide a user interface for managing student records and displaying grade reports.
- Handle file I/O for storing and loading student data persistently.
Real-world application:
- Educational institutions: They can use such a grade checker for students to access their grades from anywhere
- Online learning portals: These sites implement such functionalities to provide students with immediate feedback on their courses
- Personal use: You can use this system to keep track of your own grades and check your improvements
Data Structure project for intermediate with source code
5. Plagiarism detection system
Difficulty level: Medium
Writers face difficulty in detecting plagiarism in their work, which leads to legal issues at times. This tool will help you analyze documents, implement algorithms to compare them with other text documents, and detect similarities within them.
Learning outcomes:
- Implementing string-matching algorithms for the identification of plagiarism
- Use of data structures like hash tables to store and manipulate data for comparison
- Optimizing algorithms to handle large volumes of text data efficiently
- Gain knowledge of data preprocessing, text normalization, and handling large datasets
What it takes to execute this project:
- Use a data structure like a hash table or trie to store and index document text.
- Implement algorithms for text preprocessing (tokenization, stemming, stop word removal).
- Utilize string matching algorithms (e.g., Knuth-Morris-Pratt, Boyer-Moore) for finding plagiarized text segments.
- Implement similarity measures like cosine similarity, Jaccard index, or n-gram matching.
- Use clustering algorithms (e.g., k-means, hierarchical) to group similar documents.
- Implement efficient data structures (e.g., suffix arrays, suffix trees) for pattern matching.
- Handle large document collections using techniques like shingling or locality-sensitive hashing.
- Provide a user interface for uploading documents and displaying plagiarism detection results.
- Integrate with external document sources or databases for comprehensive checking.
Real-world application:
- Educational institutions: They can use this application to look into academic papers and students’ assignments to detect plagiarism
- Publishing house: These houses need to publish articles and videos, and they can use the application to ensure the content is free from plagiarism
- Content creators: Content creators need to check for plagiarism in their content before submitting it to clients or releasing it on different platforms, and therefore, this application is useful for them
- Legal house: Lawyers use this application to analyze contracts, patents, or other legal documents for plagiarism or intellectual property violations
6. Banking management system
Difficulty level: Medium
This project will train students to create an application that will replicate banking operations like account creation, balance inquiry, deposit and withdrawal of funds, and transfer of money. It requires designing and implementing data structures to store and manage account information efficiently and developing algorithms to handle banking transactions securely and accurately.
Learning outcomes:
- Understanding and implementing multiple data structures, like linked lists, arrays, and hash tables, to store and manage user details
- Developing and optimizing algorithms for handling banking operations using searching, shorting, and data manipulation techniques and ensuring transactions are processed efficiently
- Knowledge of database management for storing and retrieving account information
- Handling multiple transactions simultaneously and ensuring data consistency and integrity
What it takes to execute this project:
- Identify core banking operations like account management, transaction handling, reporting, etc.
- Choose appropriate data structures, such as arrays/lists for accounts, queues/stacks for transactions, and trees/graphs for hierarchical data.
- Implement account management using arrays/lists: create, update, and delete accounts.
- Handle transactions like deposits, withdrawals, and money transfers using queues/stacks.
- Use sorting algorithms for report generation, e.g., merge/quick sort for account balances.
- Implement search algorithms like binary search for account lookup.
- Utilize graphs/trees for organizational hierarchy and access control.
- Ensure data integrity with error handling and input validation.
Real world application:
- Personal use: This application can help you manage money transfers and check your account balance at your comfort
- E-commerce platforms: They can implement this system for secured payments for online purchases
7. Travel planner using Graph
Difficulty level: Medium
This project aims to help travelers plan optimal travel routes between different locations. It uses graph data structures to represent locations as nodes and paths and the connecting roads or transportation routes as edges. This application will be able to calculate the shortest path between two locations, considering factors like distance, travel time, and cost.
Learning outcomes:
- Hands-on experience in working with graph data structures and algorithms
- Learn different types of graphs like directed, undirected, and weighted graphs
- How to represent and traverse graphs using techniques like adjacency lists or matrices
- Implement graph algorithms like Dijkstra’s algorithm, DFS (Depth-First-Search), and BFS (Breadth-First-Search) to find the shortest path between two locations
- Develop skills in data handling, input/output operations, and user interface design
What it takes to execute this project:
- Represent locations as nodes in a graph data structure, with edges representing travel routes.
- Assign weights to edges based on distance, travel time, or cost.
- Implement graph traversal algorithms like depth-first search (DFS) or breadth-first search (BFS) to find possible routes.
- Use shortest path algorithms like Dijkstra’s or A* to find optimal routes based on distance/time/cost.
- Implement data structures like priority queues or heaps for efficient path-finding.
- Handle constraints like budget, travel mode, or stopover preferences.
- Integrate with external APIs for fetching location data, travel information, or real-time updates.
- Provide a user interface for entering source, destination, and preferences, and displaying planned routes.
Real world application:
- Navigation systems: Navigation systems like GPS devices and maps can implement this application to find the best roads for travelers and drivers
- Logistics and supply chain industry: They can use such systems to optimize their delivery routes for trucks to reduce cost and improve delivery time
- Travel agencies: These organizations can provide better services by planning efficient travel roads for clients considering different constraints with this application
8. Cash flow minimizer
Difficulty level: Medium
This cash flow minimizer project focuses on developing a program that will minimize the cash flow among a group of individuals who have borrowed money from each other. It will use graph algorithms with optimization techniques like the Min-Cost Max-Flow algorithm to settle debts in minimum transactions.
Learning outcomes:
- Learn multiple data structures and algorithms related to cash flow management and optimization problems
- Use of efficient data structures like trees or priority queues for storing and manipulating data-based transactions
- Gain experience in implementing sorting and searching algorithms and greedy optimization techniques
- Handling large volumes of data, input/output operations, and designing user interface
What it takes to execute this project:
- Represent cash flow data as a time series or sequence using an appropriate data structure (e.g., array, linked list).
- Implement algorithms to identify and categorize income and expense transactions.
- Use sorting algorithms (e.g., merge sort, quick sort) to order transactions chronologically.
- Implement data structures like stacks or queues to handle transaction dependencies and scheduling.
- Utilize dynamic programming or greedy algorithms to minimize cash flow by rescheduling transactions.
- Incorporate constraints like due dates, penalties, and interest rates.
- Provide a user interface for inputting transaction data and displaying optimized cash flow schedules.
- Handle error checking and input validation for transaction data.
- Consider integrating with financial databases or APIs for real-world data.
Real-world application:
- Financial institutions: These bodies can use such systems to optimize their interbank debt settlement process, reducing transaction costs and complexity
- Corporate companies: They can implement such a system to manage internal transfers and settlements between multiple departments or subsidiaries, enhancing cash flow management
- Personal use: You can use such a system for expense sharing with minimum cash flow
9. Building a crossword puzzle game
Difficulty level: Medium
Crossword puzzle games are often liked by children, and even parents allow them to play them to improve their vocabulary and language skills. This program will train students to build a crossword puzzle game that can generate and solve various puzzles. It will take a list of words, generate a crossword grid, and place the words in an interlocking pattern. It will also help users find and fill in the missing words depending on clues and the interlocking pattern of the grid.
Learning outcomes:
- Learn multiple data structures and algorithms related to string manipulation, search techniques, and pattern matching
- Use data structures like hash tables for storing and manipulating word lists
- Implementing backtracking algorithms or constraint satisfaction techniques to generate and solve different crossword puzzles
- Designing a user-friendly interface for users to interact with the system
What it takes to execute this project:
- Use a 2D array or matrix to represent the crossword puzzle grid.
- Implement a trie or prefix tree data structure to store and search for words efficiently.
- Utilize backtracking algorithms to find valid word placements in the grid.
- Implement constraint satisfaction techniques to ensure word intersections and grid validity.
- Use heuristics or scoring functions to guide the search process and optimize solutions.
- Integrate randomization algorithms for puzzle generation and variation.
- Implement data structures like stacks or queues for undo/redo functionality.
- Provide a user interface for displaying the puzzle, entering words, and interacting with game features.
- Handle input validation, error checking, and scoring mechanisms for gameplay.
Real-world application:
- Play schools: They can use this tool for building vocabulary and language skills among children
- E-commerce platforms: They can implement such games to attract customers to their platform and let them win cashback or discount coupons
- Publishing houses: Newspapers and magazines often print crossword puzzles for readers to improve their vocabulary, and with this tool, creating new puzzles regularly becomes easy
10. Library management system
Difficulty level: Medium
This project trains students to develop a program that can efficiently manage library operations like cataloging books, tracking book borrowing and returns, member record maintenance, generating reports on library statistics, etc.
Learning outcomes:
- Use of data structures like linked lists, hash tables, trees, and queues to manage and organized library information
- Understanding of database concepts like CRUD (Create, Read, Update, and Delete) operations and ways to interact with a database
- Implementing efficient searching and sorting algorithms to search for books and borrower information and sort records based on different criteria
- Handling complex queries and operations within the library system
What it takes to execute this project:
- Use a data structure like an array, linked list, or hash table to store book records (title, author, availability).
- Implement functions to add, remove, and search for books by title, author, or other criteria.
- Use sorting algorithms like merge sort or quick sort to arrange books alphabetically or by other attributes.
- Implement search algorithms like binary search or linear search for efficient book lookup.
- Consider using a tree data structure like a binary search tree or trie for hierarchical organization and efficient searches.
- Implement data structures like queues or priority queues for handling book reservations and waitlists.
- Handle member management, book lending/return, and fine calculation algorithms.
- Provide a user interface for librarians and members to interact with the system.
Real-world application:
- Educational institutions: They can use this system to automate their library operations, like book cataloging, inventory management, and report generation
- Public libraries: These organizations need to manage large collections of books, and implementing this system can help them handle the check-in and check-out processes efficiently
- Digital libraries: These perform all their work online, and this library management system can help them manage ebooks and other digital resources and provide search and book access abilities to users
11. Building a File Zipper
Difficulty level: Medium
This project focuses on building a file compression tool using a Greedy Huffman encoder or Run-Length Encoding. This File Zipper tool compresses a large file or directory into a single compressed file that saves your memory storage. These compressed files are usually of smaller sizes and are easy to transfer from one machine to another.
Learning outcomes:
- Knowledge of algorithms like Huffman coding, RLE (Run-Length Encoding), or LZW (Lempel-Ziv-Welch), required for compressing and decompressing files
- Understanding binary trees and the Tries data structures particularly used in Huffman coding to efficiently encode and decode your data
- Gain experience with handling bits and bytes for file compression
- Knowledge of designing user interfaces for users to interact with the system
What it takes to execute this project:
- Use a data structure like a binary tree or Huffman coding tree to represent file contents and generate compression codes.
- Implement algorithms like Huffman coding or LZW (Lempel-Ziv-Welch) for data compression and decompression.
- Utilize file I/O operations to read and write compressed/decompressed data to files.
- Implement data structures like arrays or linked lists to store compressed data and file metadata.
- Handle input validation, error checking, and progress reporting for compression/decompression operations.
- Provide a user interface for selecting files, configuring compression settings, and initiating compression/decompression.
- Consider multi-threaded or parallel processing techniques for improved performance on large files.
- Implement archiving and extraction algorithms for handling multiple files in a single archive.
Real-world application:
- Personal or business use: This tool can be efficiently used for data backups and archives as they use less memory space
- Software developers: These professionals can use the tool for data storage and transferring large volumes of data quickly over networks
Data Structure project for final year students with source code
12. Real estate property search
Difficulty level: Hard
This project uses the R-tree indexing technique to handle spatial queries and provide relevant property listings to users depending on multiple criteria. Students will start doing the project by collecting real estate property data, including location, property price, property area, number of bedrooms, etc., and then use the R-tree index to organize data spatially. Now, to represent each of these properties, you need spatial objects. Then, these objects will be arranged on an R-tree in a hierarchical structure for efficient spatial queries.
Learning outcomes:
- Knowledge of data structures like R-trees used for indexing multidimensional information
- Implement efficient searching and sorting algorithms to handle user queries and retrieve relevant property listings
- Understanding of multiple database operations and indexing to store and query large data sets efficiently
- Optimizing search and retrieval processes for better performance and improved user experience
What it takes to execute this project:
- Use a data structure like a hash table or tree (e.g., B-tree, R-tree) to store and index property data efficiently.
- Implement search algorithms like range queries, nearest neighbor searches, or spatial queries to find properties based on location, price, area, etc.
- Utilize sorting algorithms (e.g., merge sort, quick sort) to order search results based on user preferences.
- Implement data structures like priority queues or heaps for ranking and displaying top results.
- Handle multi-criteria searches by combining multiple filters and conditions.
- Integrate with external APIs or databases to fetch property data and make real-time updates.
- Provide a user interface for entering search criteria, displaying results, and interacting with property details.
- Implement caching or indexing techniques for improving search performance on large datasets.
Real-world application:
- Real estate online portals: These websites can integrate such systems to help users find properties that match their criteria and provide valuable service to property buyers and sellers
- Property Management agency: Such an organization can use the system to manage their real-time property listings and provide the best property solutions to their clients
- Research purposes: Researchers can use this tool for analyzing the real estate property market and identify trends for making better decisions like increasing the property price of a particular area in demand
13. Real-time traffic analysis
Difficulty level: Hard
This project will help users to analyze and monitor real-time traffic data. Students will collect data from sensors, cameras, or GPS devices and use segment trees to efficiently process and analyze them. Once the data is collected, they can make queries to retrieve that data, such as the average speed of a car at a specific time interval. This system will also provide insights and recommendations for optimizing traffic flow in an area and improving its transportation infrastructure.
Learning outcomes:
- Understanding of various data structures and algorithms related to data processing, analysis, and visualization
- Handling continuous data streams and real-time data updates
- Using graphs to model road networks and implement algorithms like Dijkstra’s algorithm to calculate the shortest path
- Use of heap and priority queues to manage dynamic data for real-time traffic updates and route optimization
- Implementing concurrent algorithms to process multiple data streams simultaneously
What it takes to execute this project:
- Use a graph data structure to represent the road network, with nodes for intersections and edges for road segments.
- Implement data structures like queues or priority queues to model and simulate traffic flow.
- Utilize shortest path algorithms like Dijkstra’s or A* to calculate optimal routes and travel times.
- Implement algorithms for traffic assignment and route choice modeling, such as user equilibrium or system optimal approaches.
- Handle real-time updates and dynamic changes in traffic conditions using appropriate data structures and algorithms.
- Integrate with external data sources (e.g., sensors, APIs) for real-time traffic data ingestion.
- Provide a user interface for visualizing traffic conditions, analyzing bottlenecks, and generating reports or alerts.
- Consider distributed or parallel computing techniques for handling large-scale traffic data and simulations.
Real-world application:
- Traffic management authorities: They can use this tool to efficiently manage traffic congestion, monitor road conditions, and optimize traffic flow
- Navigation systems: They can integrate with such tools for real-time traffic analysis and help drivers make better road decisions
- Emergency service providers: These people can use such tools to find the quickest route during emergencies
- Urban planning bodies: They use such tools to analyze the traffic pattern of an area to make decisions about its infrastructure development or improving traffic networks
14. Email Spam Filter
Difficulty level: Hard
The email spam filter project will train students to build a program that filters incoming emails as spam or non-spam based on specific criteria and patterns. Bloom filters can be used for this purpose.
First, you need to gather data from email addresses or domains associated with spam emails. Then, initialize your bloom filter with an appropriate size and number of hash functions and the known spam email patterns. Each of these patterns will be hashed by selected hash functions, and the corresponding bits in the filter will be set to 1. When a new email arrives, the address is hashed, and the filter determines whether its corresponding bits are set to 1. If all of them are set to 1, the email is considered spam.
Learning outcomes:
- Data processing and analyzing techniques, including tokenization, stemming, and feature extraction
- Understanding of statistical methods like Naive Bayes or decision trees for classification of data
- Using hash tables to store and retrieve data and their corresponding bits
What it takes to execute this project:
- Use a data structure like a hash table or trie to store and index spam words or patterns.
- Implement text preprocessing techniques like tokenization, stemming, and stop word removal.
- Utilize machine learning algorithms like Naive Bayes, Support Vector Machines, or Decision Trees for spam classification.
- Implement feature extraction and selection algorithms to identify relevant email attributes for classification.
- Use data structures like sets or bloom filters for efficient spam word lookup and matching.
- Handle large email datasets using techniques like reservoir sampling or online learning algorithms.
- Provide a user interface for configuring spam filtering rules, whitelists/blacklists, and displaying classification results.
- Integrate with email clients or servers for real-time spam filtering and email processing.
Real-world application:
- Personal and business use: You can use this system to filter out spam emails and protect all your accounts from phishing activities
- Email service providers: They can integrate such systems to improve their spam detection services and enhance user experience
- Social media forums: These sites can integrate similar systems to filter spammers’ emails
15. Building a Web Crawler
Difficulty level: Hard
This project will develop a program that can automatically traverse and collect data from websites by following hyperlinks and downloading web pages. You start from a given URL (or set of URLs), retrieve the web page content, extract and follow the links on that page, and repeat the process recursively to crawl through an entire website or a specified domain.
Learning outcomes:
- Knowledge of various data structures and algorithms related to web scraping, graph traversal, and URL handling.
- Learn data structures like queues, sets, or graphs for storing and managing URLs and web page data
- Gain experience in implementing graph traversal algorithms like breadth-first search (BFS) or depth-first search (DFS) for crawling websites.
- Develop skills in handling network requests, parsing HTML/XML data, and dealing with web-related challenges like broken links, robots.txt files, and rate-limiting
What it takes to execute this project:
- Use a queue or priority queue data structure to store URLs to be crawled.
- Implement web page fetching and parsing algorithms using techniques like HTML parsing or regular expressions.
- Utilize graph data structures like adjacency lists or matrices to represent website links and structure.
- Implement graph traversal algorithms like breadth-first search (BFS) or depth-first search (DFS) for crawling and link extraction.
- Use sets or bloom filters to avoid revisiting already crawled pages.
- Implement URL normalization, deduplication, and link extraction algorithms.
- Handle multi-threading or distributed crawling for improved performance and scalability.
- Provide a user interface for configuring crawl settings, scheduling, and monitoring progress.
- Integrate with databases or search engines to store and index crawled data.
Real-world application:
- Search engines: Different search engines like Google and Bing can use it for web page indexing and web content discovery
- Research purposes: Researchers can use it to find relevant papers and web content for their research work
- SEO analysis: It can be used by SEO experts to analyze web structure and content for search engine optimization purposes
What are the benefits of doing data structure projects for students?
1. Better understanding of data structures and algorithms
Having theoretical knowledge won’t give you a clear understanding of how data structure and algorithms work in real-world scenarios. Therefore, we believe students need to practice implementing their skills in multiple projects regularly.
2. Skill development
When you practice these projects under experts, they train you how to solve the challenges that may appear while executing a program.
You learn how to test your system with different datasets, fix different kinds of bugs, and ensure it meets client requirements.
3. Enhance professional resume
You can add these projects to your resume to enhance your impact on potential recruiters. Otherwise, you can create a GitHub account and post your projects on them.
Then, share your account link on the resume, allowing recruiters to visit your account and check your skills and expertise.
4. Build innovation and creativity
Constant practice with DSA projects improves your innovation and creativity. It allows you to implement optimal algorithms and suitable data structures into projects that save time and effort.
Becoming a Data Structure expert in the next 3 months with Codegnan
At Codegnan, we believe nothing is impossible if you are interested in learning a new skill. We don’t restrict anyone, even if you don’t have a tech background, from enrolling in our courses. This data structure and algorithms course is for individuals who want to start their programming career in a few months.
Here’s wwhy enroll in our data structure training course:
- Complete the course in 3 months under experts from top universities like IIT and Stanford University or working professionals in the industry
- The course is available at ₹30,000, and you can often avail of our discounts to learn at affordable prices
- We cover basic to advanced concepts all in one package with hands-on training on live projects
- You can test your clarity with practice assignments available after every class
- Clear your doubts immediately during the doubt-clearing sessions with our experts
- Get certified after course completion and a chance to get placed in top companies
- Gain full stack development knowledge with our present Job Accelerator Program and land a job immediately after course completion
- Chance to learn multiple other programming languages or trending technologies online from our experts
Sairam Uppugundla is the CEO and founder of Codegnan IT Solutions. With a strong background in Computer Science and over 10 years of experience, he is committed to bridging the gap between academia and industry.
Sairam Uppugundla’s expertise spans Python, Software Development, Data Analysis, AWS, Big Data, Machine Learning, Natural Language Processing (NLP) and more.
He previously worked as a Board Of Studies Member at PB Siddhartha College of Arts and Science. With expertise in data science, he was involved in designing the Curriculum for the BSc data Science Branch. Also, he worked as a Data Science consultant for Andhra Pradesh State Skill Development Corporation (APSSDC).
