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Fees
₹2,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Fees
₹2,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Seats
100
Students
300
Seats
100
Students
300
The Computer Applications program at Lnct Vidhyapeeth University Indore follows a structured curriculum spanning eight semesters, integrating foundational knowledge with specialized electives and practical applications. This carefully curated sequence ensures students develop a strong base in core computing concepts before advancing into domain-specific expertise.
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| CS102 | Programming and Problem Solving | 3-1-0-4 | - |
| CS103 | Computer Organization | 3-1-0-4 | CS102 |
| CS104 | Introduction to Data Structures and Algorithms | 3-1-0-4 | CS102 |
| CS105 | Discrete Mathematics | 3-1-0-4 | - |
| CS106 | English for Technical Communication | 2-0-0-2 | - |
| CS107 | Engineering Graphics and Design | 2-0-0-2 | - |
| CS108 | Lab: Programming and Problem Solving | 0-0-3-1 | CS102 |
| CS109 | Lab: Data Structures and Algorithms | 0-0-3-1 | CS104 |
| CS110 | Introduction to Computer Science | 2-0-0-2 | - |
| CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| CS202 | Data Structures and Algorithms II | 3-1-0-4 | CS104 |
| CS203 | Digital Logic and Computer Design | 3-1-0-4 | CS103 |
| CS204 | Object Oriented Programming | 3-1-0-4 | CS102 |
| CS205 | Database Management Systems | 3-1-0-4 | CS104 |
| CS206 | Probability and Statistics | 3-1-0-4 | CS101 |
| CS207 | Lab: Object Oriented Programming | 0-0-3-1 | CS204 |
| CS208 | Lab: Database Management Systems | 0-0-3-1 | CS205 |
| CS209 | Software Engineering Principles | 3-1-0-4 | CS204 |
| CS210 | Operating Systems | 3-1-0-4 | CS103 |
| CS301 | Engineering Mathematics III | 3-1-0-4 | CS201 |
| CS302 | Computer Networks | 3-1-0-4 | CS210 |
| CS303 | Design and Analysis of Algorithms | 3-1-0-4 | CS202 |
| CS304 | Compiler Design | 3-1-0-4 | CS205 |
| CS305 | Web Technologies | 3-1-0-4 | CS204 |
| CS306 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS205 |
| CS307 | Lab: Web Technologies | 0-0-3-1 | CS305 |
| CS308 | Lab: Compiler Design | 0-0-3-1 | CS304 |
| CS309 | Information Security | 3-1-0-4 | CS205 |
| CS310 | Human Computer Interaction | 3-1-0-4 | - |
| CS401 | Data Science and Big Data Analytics | 3-1-0-4 | CS206 |
| CS402 | Mobile Application Development | 3-1-0-4 | CS204 |
| CS403 | Internet of Things (IoT) | 3-1-0-4 | CS210 |
| CS404 | Cloud Computing | 3-1-0-4 | CS210 |
| CS405 | Software Testing and Quality Assurance | 3-1-0-4 | CS209 |
| CS406 | Natural Language Processing | 3-1-0-4 | CS205 |
| CS407 | Lab: Mobile Application Development | 0-0-3-1 | CS402 |
| CS408 | Lab: Cloud Computing | 0-0-3-1 | CS404 |
| CS409 | Advanced Topics in Computer Science | 3-1-0-4 | CS205 |
| CS410 | Capstone Project | 3-0-0-6 | CS305, CS309, CS401 |
The department offers several advanced elective courses designed to deepen students' understanding of specialized areas in computer applications:
This course introduces students to the fundamental concepts of AI, including search algorithms, knowledge representation, planning, and machine learning techniques. Students will learn about supervised and unsupervised learning, neural networks, deep learning frameworks, and applications in natural language processing and computer vision.
This elective focuses on the tools and methods used for analyzing large datasets. Topics include data mining, statistical modeling, visualization techniques, and big data platforms such as Hadoop and Spark. Students will gain hands-on experience with Python, R, and SQL for data manipulation and analysis.
This course covers the development of mobile applications for Android and iOS platforms. Students will learn about UI/UX design principles, cross-platform frameworks like Flutter and React Native, app deployment, and monetization strategies in mobile ecosystems.
This course explores the architecture and protocols used in IoT systems. It covers sensor networks, embedded systems programming, cloud integration, security challenges in IoT environments, and real-world applications in smart cities, agriculture, and healthcare.
This elective delves into cloud infrastructure, service models (IaaS, PaaS, SaaS), virtualization technologies, containerization with Docker and Kubernetes, and deployment strategies for scalable applications. Students will work on projects involving AWS, Azure, and Google Cloud Platform.
This course teaches students how to ensure software quality through various testing methodologies, including unit testing, integration testing, system testing, and performance testing. It also covers automation tools, bug tracking systems, and quality metrics used in agile development environments.
This advanced elective focuses on techniques for processing human language using computational methods. Topics include text classification, sentiment analysis, machine translation, named entity recognition, and deep learning models for NLP tasks such as question answering and summarization.
The department places significant emphasis on project-based learning to enhance practical understanding and foster innovation among students. The curriculum includes mandatory mini-projects in the second and third years, followed by a capstone project in the final year.
Mini projects are designed to give students hands-on experience with real-world problem-solving. Students form teams of 3-5 members and work on projects under faculty supervision for 6 weeks. The evaluation includes project presentations, documentation quality, and peer reviews.
In the final year, students undertake a comprehensive capstone project that integrates all aspects of their learning. Projects can be theoretical or applied, focusing on emerging technologies or industry challenges. Students are paired with faculty mentors who guide them through the research and implementation phases.
Students select projects based on their interests and career goals, with guidance from faculty advisors. The selection process involves proposal submission, review by a committee, and approval before project initiation. Faculty mentors are assigned based on project relevance and mentor availability.
