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Fees
₹4,50,000
Placement
95.0%
Avg Package
₹7,50,000
Highest Package
₹12,00,000
Fees
₹4,50,000
Placement
95.0%
Avg Package
₹7,50,000
Highest Package
₹12,00,000
Seats
120
Students
1,200
Seats
120
Students
1,200
The Computer Applications program at Mnr University Telangana is structured over 8 semesters, with a carefully curated blend of core courses, departmental electives, science electives, and laboratory sessions. The curriculum is designed to provide students with a strong foundation in computer science principles while allowing them to specialize in their areas of interest. Each semester is carefully planned to ensure a smooth progression from fundamental concepts to advanced topics, with continuous assessment and project-based learning integrated throughout the program.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | CS102 | Physics for Computer Applications | 3-1-0-4 | None |
| 1 | CS103 | Basic Programming in C | 2-0-2-4 | None |
| 1 | CS104 | Introduction to Computing | 2-0-2-4 | None |
| 1 | CS105 | English for Technical Communication | 2-0-0-2 | None |
| 1 | CS106 | Physical Education | 0-0-0-2 | None |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Chemistry for Computer Applications | 3-1-0-4 | None |
| 2 | CS203 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| 2 | CS204 | Object Oriented Programming in C++ | 2-0-2-4 | CS103 |
| 2 | CS205 | Computer Organization and Architecture | 3-1-0-4 | CS102 |
| 2 | CS206 | Discrete Mathematics | 3-1-0-4 | CS101 |
| 3 | CS301 | Database Management Systems | 3-1-0-4 | CS203 |
| 3 | CS302 | Operating Systems | 3-1-0-4 | CS205 |
| 3 | CS303 | Computer Networks | 3-1-0-4 | CS205 |
| 3 | CS304 | Software Engineering | 3-1-0-4 | CS204 |
| 3 | CS305 | Probability and Statistics | 3-1-0-4 | CS201 |
| 3 | CS306 | Web Technologies | 2-0-2-4 | CS204 |
| 4 | CS401 | Artificial Intelligence | 3-1-0-4 | CS301 |
| 4 | CS402 | Machine Learning | 3-1-0-4 | CS305 |
| 4 | CS403 | Cybersecurity Fundamentals | 3-1-0-4 | CS303 |
| 4 | CS404 | Data Science and Analytics | 3-1-0-4 | CS305 |
| 4 | CS405 | Cloud Computing | 3-1-0-4 | CS303 |
| 4 | CS406 | Internet of Things | 3-1-0-4 | CS303 |
| 5 | CS501 | Advanced Algorithms | 3-1-0-4 | CS203 |
| 5 | CS502 | Computer Vision | 3-1-0-4 | CS401 |
| 5 | CS503 | Deep Learning | 3-1-0-4 | CS402 |
| 5 | CS504 | Network Security | 3-1-0-4 | CS303 |
| 5 | CS505 | Big Data Analytics | 3-1-0-4 | CS404 |
| 5 | CS506 | Software Testing and Quality Assurance | 3-1-0-4 | CS304 |
| 6 | CS601 | Research Methodology | 2-0-2-4 | CS501 |
| 6 | CS602 | Advanced Software Engineering | 3-1-0-4 | CS304 |
| 6 | CS603 | Mobile Application Development | 3-1-0-4 | CS306 |
| 6 | CS604 | Human Computer Interaction | 3-1-0-4 | CS501 |
| 6 | CS605 | Game Development | 3-1-0-4 | CS401 |
| 6 | CS606 | Database Design and Optimization | 3-1-0-4 | CS301 |
| 7 | CS701 | Capstone Project I | 4-0-0-4 | CS501 |
| 7 | CS702 | Capstone Project II | 4-0-0-4 | CS701 |
| 7 | CS703 | Special Topics in Computer Applications | 3-1-0-4 | CS501 |
| 7 | CS704 | Internship | 0-0-0-8 | CS501 |
| 8 | CS801 | Advanced Capstone Project | 6-0-0-6 | CS702 |
| 8 | CS802 | Industry Project | 4-0-0-4 | CS702 |
| 8 | CS803 | Professional Ethics and Social Responsibility | 2-0-0-2 | None |
| 8 | CS804 | Entrepreneurship and Innovation | 2-0-0-2 | None |
The department offers a range of advanced departmental electives that allow students to explore specialized areas of interest and develop expertise in emerging technologies. These courses are designed to provide in-depth knowledge and practical skills that are highly relevant to the current industry landscape.
This course provides a comprehensive introduction to neural networks and deep learning techniques. Students will learn about feedforward networks, convolutional neural networks, recurrent neural networks, and transformers. The course emphasizes practical implementation using frameworks like TensorFlow and PyTorch, with hands-on labs and projects. The learning objectives include understanding the mathematical foundations of neural networks, implementing various architectures, and applying deep learning techniques to real-world problems. This course is particularly relevant for students interested in artificial intelligence, machine learning, and data science.
Computer Vision is a rapidly growing field that focuses on enabling computers to interpret and understand visual information from the world. This course covers fundamental concepts in image processing, feature extraction, object detection, and recognition. Students will learn to implement computer vision algorithms using libraries like OpenCV and scikit-image. The course includes practical projects involving image classification, object tracking, and scene understanding. The learning objectives include understanding image processing techniques, implementing computer vision algorithms, and applying these to real-world applications such as autonomous vehicles, medical imaging, and surveillance systems.
This course delves into advanced cybersecurity concepts and techniques, including network security, cryptography, and ethical hacking. Students will learn about advanced persistent threats, intrusion detection systems, and security frameworks. The course includes hands-on labs with tools like Kali Linux, Metasploit, and Wireshark. The learning objectives include understanding advanced security concepts, conducting vulnerability assessments, and implementing security measures to protect digital assets. This course is essential for students who wish to pursue careers in cybersecurity consulting, threat analysis, and information assurance.
Data Mining and Big Data Analytics are critical skills in today's data-driven world. This course covers techniques for extracting knowledge from large datasets, including association rule mining, clustering, classification, and prediction. Students will learn to use tools like Hadoop, Spark, and Python libraries for data analysis. The course emphasizes practical implementation and real-world applications in various domains such as marketing, healthcare, and finance. The learning objectives include understanding data mining techniques, implementing big data processing pipelines, and applying analytical methods to solve business problems.
Cloud Computing and DevOps are essential for modern software development and deployment. This course covers cloud architecture, virtualization, containerization, and microservices. Students will learn to deploy applications on platforms like AWS, Azure, and Google Cloud. The course includes hands-on labs with Docker, Kubernetes, and CI/CD pipelines. The learning objectives include understanding cloud computing models, implementing containerized applications, and automating deployment processes. This course is highly relevant for students interested in cloud architecture, software deployment, and DevOps practices.
The Internet of Things (IoT) represents a paradigm shift in how devices interact and communicate. This course covers IoT architecture, sensor networks, and embedded system design. Students will learn to develop IoT applications using platforms like Arduino, Raspberry Pi, and ESP32. The course includes practical projects involving smart home systems, environmental monitoring, and industrial automation. The learning objectives include understanding IoT architecture, designing embedded systems, and implementing IoT solutions for real-world applications.
Software Testing and Quality Assurance are critical for ensuring software reliability and performance. This course covers various testing methodologies, including unit testing, integration testing, and system testing. Students will learn to use testing frameworks like JUnit, Selenium, and Postman. The course includes hands-on labs with test automation and quality metrics. The learning objectives include understanding testing principles, implementing automated testing, and ensuring software quality through systematic approaches.
Human-Computer Interaction (HCI) focuses on designing user-friendly and accessible interfaces. This course covers user experience design, usability testing, and accessibility principles. Students will learn to design interfaces using tools like Figma and Sketch, and conduct user research. The course includes practical projects involving interface design, usability evaluation, and accessibility compliance. The learning objectives include understanding user needs, designing effective interfaces, and evaluating user experience through systematic methods.
Game Development and 3D Modeling are essential skills for students interested in interactive entertainment. This course covers game design principles, 3D modeling, and game engines like Unity and Unreal Engine. Students will learn to create interactive games and 3D applications. The course includes practical projects involving game development, 3D modeling, and animation. The learning objectives include understanding game design principles, implementing 3D graphics, and creating interactive applications using modern game engines.
Database Design and Optimization are critical for efficient data management and retrieval. This course covers database design principles, normalization, and query optimization. Students will learn to design and implement database systems using SQL and NoSQL technologies. The course includes hands-on labs with database management systems and performance tuning. The learning objectives include understanding database design principles, implementing efficient queries, and optimizing database performance.
The department's philosophy on project-based learning is centered on the belief that hands-on experience is essential for developing practical skills and deep understanding of complex concepts. The curriculum is designed to integrate project work throughout the program, from foundational courses to advanced specializations.
Mini-projects are assigned in the second and third years of the program, providing students with opportunities to apply theoretical concepts to practical problems. These projects are typically completed in groups and are designed to reinforce learning outcomes of core courses. Students are required to work on projects that involve research, design, implementation, and documentation. The evaluation criteria include technical implementation, creativity, presentation, and teamwork. Mini-projects are an integral part of the assessment process and contribute to the overall grade of the course.
The final-year capstone project is a comprehensive, individual or group project that integrates all the knowledge and skills acquired throughout the program. Students work under the guidance of faculty mentors to develop a significant project that addresses a real-world problem. The project typically involves research, design, implementation, testing, and documentation. Students are expected to present their work at a final project exhibition and defend their project in front of a panel of experts. The capstone project is a culmination of the student's academic journey and serves as a portfolio piece for future employment or further studies.
Students are encouraged to select projects that align with their interests and career aspirations. The department provides a list of project topics and faculty mentors who specialize in different areas. Students can choose to work on projects suggested by faculty or propose their own ideas. The mentorship process involves regular meetings with faculty mentors, progress reviews, and feedback sessions. The department ensures that each student is paired with a suitable mentor who can provide guidance and support throughout the project duration.
