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Fees
₹5,00,000
Placement
93.0%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Fees
₹5,00,000
Placement
93.0%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Seats
120
Students
600
Seats
120
Students
600
The Computer Science curriculum at Agrawan Heritage University Agra is designed to provide a balanced blend of theoretical knowledge and practical application. The program spans eight semesters, offering students a comprehensive foundation in computer science principles followed by specialized tracks tailored to their career aspirations.
The first year focuses on building essential skills in mathematics, physics, and introductory programming. Students are introduced to fundamental concepts such as algorithms, data structures, and problem-solving techniques. This foundational stage prepares them for advanced coursework while ensuring a smooth transition into higher-level topics.
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS101 | Introduction to Programming | 3-0-0-3 | None |
| CS102 | Mathematics for Computer Science | 3-0-0-3 | None |
| CS103 | Computer Organization | 3-0-0-3 | None |
| CS104 | Physics for Computer Science | 3-0-0-3 | None |
| CS105 | English Communication Skills | 2-0-0-2 | None |
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| CS202 | Database Management Systems | 3-0-0-3 | CS101 |
| CS203 | Software Engineering | 3-0-0-3 | CS101 |
| CS204 | Operating Systems | 3-0-0-3 | CS103 |
| CS205 | Discrete Mathematics | 3-0-0-3 | CS102 |
The second year introduces students to more advanced topics in computer science, including network architecture, system programming, and software design principles. Students also begin exploring elective subjects that align with their interests and career goals.
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS301 | Computer Networks | 3-0-0-3 | CS204 |
| CS302 | Compiler Design | 3-0-0-3 | CS201 |
| CS303 | Web Technologies | 3-0-0-3 | CS203 |
| CS304 | System Programming | 3-0-0-3 | CS204 |
| CS305 | Probability and Statistics | 3-0-0-3 | CS102 |
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS401 | Object-Oriented Programming | 3-0-0-3 | CS201 |
| CS402 | Artificial Intelligence | 3-0-0-3 | CS201 |
| CS403 | Machine Learning | 3-0-0-3 | CS201, CS305 |
| CS404 | Big Data Analytics | 3-0-0-3 | CS202, CS305 |
| CS405 | Mobile Application Development | 3-0-0-3 | CS203 |
The third year allows students to specialize in areas of interest through departmental electives, science electives, and research opportunities. This stage encourages innovation, critical thinking, and practical application of learned concepts.
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS501 | Advanced Data Structures | 3-0-0-3 | CS201 |
| CS502 | Cybersecurity Fundamentals | 3-0-0-3 | CS301 |
| CS503 | Cloud Computing | 3-0-0-3 | CS301 |
| CS504 | Human-Computer Interaction | 3-0-0-3 | CS203 |
| CS505 | Game Development | 3-0-0-3 | CS401 |
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS601 | Deep Learning | 3-0-0-3 | CS403 |
| CS602 | Blockchain Technology | 3-0-0-3 | CS503 |
| CS603 | Internet of Things (IoT) | 3-0-0-3 | CS301 |
| CS604 | Software Architecture | 3-0-0-3 | CS203 |
| CS605 | Research Methodology | 3-0-0-3 | CS201 |
The final year focuses on independent research, capstone projects, and industry exposure. Students engage in advanced coursework while working closely with faculty mentors to complete a significant project that demonstrates their expertise.
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS701 | Advanced Software Engineering | 3-0-0-3 | CS203 |
| CS702 | Project Management | 3-0-0-3 | CS203 |
| CS703 | Mini Project | 0-0-6-3 | CS201, CS203 |
| CS704 | Research Internship | 0-0-6-3 | CS505 |
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS801 | Final Year Thesis/Capstone Project | 0-0-12-6 | CS703 |
| CS802 | Industry Exposure Program | 0-0-6-3 | CS701 |
| CS803 | Professional Development | 2-0-0-2 | None |
Students are encouraged to explore advanced elective courses that align with their interests and career goals. These courses provide specialized knowledge in emerging areas of computer science, offering opportunities for deeper understanding and practical application.
This course covers the fundamentals of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement models using frameworks like TensorFlow and PyTorch, and apply them to real-world problems such as image classification, natural language processing, and speech recognition.
The course emphasizes hands-on experience with datasets from Kaggle and research papers. Students work on projects involving facial recognition, medical imaging, and autonomous vehicle perception systems. By the end of the course, students are proficient in building, training, and evaluating deep learning models using modern tools and techniques.
This elective explores the underlying principles of blockchain technology, including distributed consensus mechanisms, smart contracts, and cryptographic protocols. Students study both theoretical concepts and practical implementations, gaining insights into how blockchains can be used in supply chain management, digital identity verification, and financial services.
The course includes laboratory sessions where students develop simple blockchain applications using Ethereum and Hyperledger Fabric. They also analyze existing blockchain projects and propose improvements or novel use cases. The final project involves designing a blockchain-based solution for a specific industry problem.
This course introduces students to the architecture, protocols, and applications of IoT systems. It covers sensor networks, embedded systems programming, wireless communication standards, and edge computing models. Students learn to design and deploy IoT solutions for smart cities, industrial automation, and wearable technology.
Laboratory exercises involve building prototypes using Raspberry Pi, Arduino, and other microcontrollers. The course includes discussions on privacy concerns, security vulnerabilities, and scalability issues in IoT deployments. Students also explore real-world case studies from companies like Siemens, General Electric, and Cisco.
This advanced elective focuses on the design and implementation of large-scale software systems. Students learn about architectural patterns, scalability principles, microservices, containerization, and cloud-native development. The course emphasizes best practices in system design and includes case studies from leading tech companies.
Students work on a capstone project involving the design of a scalable software system for a real-world application. They apply concepts such as event-driven architecture, caching strategies, and load balancing to create robust, maintainable, and efficient systems. The course also covers tools like Kubernetes, Docker, and AWS for deployment and management.
This course prepares students for conducting independent research in computer science. It covers literature review techniques, experimental design, data analysis methods, and scientific writing skills. Students learn how to formulate hypotheses, design experiments, collect and interpret data, and present findings effectively.
The course includes guest lectures from faculty members who lead research projects in AI, cybersecurity, and software engineering. Students engage in group discussions, peer reviews, and presentations to refine their analytical and communication abilities. The final component involves developing a research proposal that aligns with their interests and career goals.
The department's philosophy on project-based learning emphasizes experiential education that bridges theory and practice. Students engage in mini-projects throughout the program, culminating in a final-year thesis or capstone project that showcases their expertise and innovation.
The mini-project phase provides students with an opportunity to apply knowledge gained from earlier semesters to solve real-world problems. Each project is supervised by a faculty mentor who guides students through the process of defining objectives, designing solutions, implementing prototypes, and presenting outcomes.
Mini projects typically span two to three months and involve teams of 3-5 students. Students select topics based on their interests or industry needs, ensuring relevance and engagement. The evaluation criteria include technical depth, creativity, teamwork, documentation quality, and presentation skills.
The final-year project is the culmination of a student’s academic journey in computer science. It requires students to conduct independent research or develop a comprehensive software solution that demonstrates mastery of core concepts and specialized knowledge.
Students choose their projects based on faculty expertise, industry relevance, and personal interest. The project is supervised by a faculty advisor who provides guidance throughout the development cycle. Evaluation includes milestone reviews, peer feedback, final presentation, and technical documentation.
The department facilitates project selection through workshops, seminars, and interaction with industry partners. Students are encouraged to collaborate with external organizations, participate in hackathons, and contribute to open-source initiatives to enhance their experience and visibility.
