Curriculum Overview
The curriculum for the BSc Computer Science program at Heritage Institute of Technology Cuttack is meticulously structured to provide a balanced blend of theoretical knowledge and practical skills. It spans eight semesters, with each semester designed to build upon previous learning and introduce new concepts relevant to the field.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics I | 4-0-0-4 | - |
| 1 | CS103 | Physics for Computer Science | 3-0-0-3 | - |
| 1 | CS104 | Chemistry for Computer Science | 3-0-0-3 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Mathematics II | 4-0-0-4 | CS102 |
| 2 | CS203 | Computer Organization and Architecture | 3-0-0-3 | - |
| 2 | CS204 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| 3 | CS301 | Databases and SQL | 3-0-0-3 | CS201 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS203 |
| 3 | CS303 | Computer Networks | 3-0-0-3 | CS201 |
| 3 | CS304 | Mathematics III | 4-0-0-4 | CS202 |
| 4 | CS401 | Software Engineering | 3-0-0-3 | CS204 |
| 4 | CS402 | Web Technologies and Development | 3-0-0-3 | CS204 |
| 4 | CS403 | Discrete Mathematics and Logic | 3-0-0-3 | CS202 |
| 4 | CS404 | Mathematics IV | 4-0-0-4 | CS304 |
| 5 | CS501 | Machine Learning and AI Fundamentals | 3-0-0-3 | CS401 |
| 5 | CS502 | Cybersecurity Principles | 3-0-0-3 | CS303 |
| 5 | CS503 | Data Science and Analytics | 3-0-0-3 | CS401 |
| 5 | CS504 | Human-Computer Interaction | 3-0-0-3 | CS201 |
| 6 | CS601 | Advanced Algorithms and Complexity | 3-0-0-3 | CS201 |
| 6 | CS602 | Cloud Computing and DevOps | 3-0-0-3 | CS402 |
| 6 | CS603 | Embedded Systems and IoT | 3-0-0-3 | CS203 |
| 6 | CS604 | Computer Graphics and Visualization | 3-0-0-3 | CS201 |
| 7 | CS701 | Research Methodology and Project Management | 3-0-0-3 | CS401 |
| 7 | CS702 | Capstone Project I | 0-0-6-6 | CS501, CS502, CS503 |
| 8 | CS801 | Capstone Project II | 0-0-6-6 | CS702 |
Advanced Departmental Elective Courses
1. Advanced Machine Learning and Deep Learning
This course delves into advanced concepts in machine learning, including neural networks, convolutional networks, recurrent networks, and transformers. Students learn to implement complex models using frameworks like TensorFlow and PyTorch. The course emphasizes practical applications in image recognition, natural language processing, and reinforcement learning.
2. Ethical Hacking and Penetration Testing
Students explore the principles of cybersecurity and ethical hacking through hands-on labs and simulations. Topics include network scanning, vulnerability assessment, exploitation techniques, and defensive strategies. This course prepares students for certification exams like CEH (Certified Ethical Hacker) and CISSP (Certified Information Systems Security Professional).
3. Data Science and Big Data Technologies
This course introduces students to big data platforms such as Hadoop, Spark, and Kafka. Students learn to process and analyze large datasets using tools like Python, R, and SQL. Real-world projects involving social media analytics, financial forecasting, and healthcare data analysis are included.
4. Software Architecture and Design Patterns
Focused on designing scalable software systems, this course covers architectural patterns, design principles, and enterprise integration technologies. Students learn to model complex systems using UML diagrams and apply design patterns such as Singleton, Factory, and Observer.
5. Mobile Application Development
This course teaches students how to develop cross-platform mobile applications using frameworks like React Native and Flutter. Topics include user interface design, backend integration, and deployment strategies for iOS and Android platforms.
6. Computer Vision and Image Processing
Students learn to extract meaningful information from images and videos using algorithms in computer vision. The course covers image filtering, feature detection, object recognition, and deep learning-based approaches. Practical applications include medical imaging, autonomous vehicles, and surveillance systems.
7. Quantum Computing Fundamentals
This emerging field explores the principles of quantum mechanics and their application in computing. Students learn about qubits, superposition, entanglement, and quantum algorithms. The course includes simulations using IBM Qiskit and theoretical frameworks for quantum error correction.
8. Natural Language Processing and Text Mining
Students study techniques for processing and understanding human language using computational methods. Topics include sentiment analysis, named entity recognition, machine translation, and chatbots. The course integrates NLP libraries like NLTK, spaCy, and Transformers.
9. Cloud Computing and DevOps Practices
This course covers cloud infrastructure, containerization technologies (Docker), orchestration tools (Kubernetes), and CI/CD pipelines. Students gain hands-on experience deploying applications on AWS, Azure, and Google Cloud platforms.
10. Internet of Things (IoT) and Embedded Systems
Students explore the design and implementation of IoT systems using microcontrollers like Arduino and Raspberry Pi. Topics include sensor integration, wireless communication protocols, real-time operating systems, and edge computing architectures.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around experiential education that bridges theory and practice. Students are encouraged to apply their knowledge to solve real-world problems, collaborate with peers, and present solutions to industry experts.
Mini-Projects (Semesters 1-4):
- Mini-projects are introduced early in the curriculum to help students understand practical applications of theoretical concepts.
- Each mini-project is assigned a duration of 4-6 weeks and requires students to work in teams of 3-5 members.
- Projects are evaluated based on technical implementation, presentation quality, documentation, and peer feedback.
Final-Year Capstone Project:
- The capstone project is a significant component of the final year curriculum, lasting 8-10 weeks.
- Students work closely with faculty mentors to select a relevant research topic or industry challenge.
- Projects are presented at an annual showcase event attended by industry partners and alumni.
The evaluation criteria for projects include:
- Technical Excellence: Quality of code, correctness of implementation, and use of appropriate tools.
- Documentation: Clear and comprehensive project documentation including design diagrams and user manuals.
- Presentation Skills: Ability to articulate the problem, methodology, results, and future scope clearly.
- Team Collaboration: Effectiveness of teamwork and contribution of each member.