Comprehensive Course Structure
The Computer Science curriculum at Mahapurusha Srimanta Sankaradeva Viswavidyalaya Nagaon is meticulously structured to provide a balanced blend of theoretical knowledge and practical application. The program spans eight semesters, with each semester carrying specific credit distribution across core subjects, departmental electives, science electives, and laboratory components.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Mathematics for Computing | 3-1-0-4 | - |
| 1 | CS102 | Introduction to Programming | 3-0-2-5 | - |
| 1 | CS103 | Data Structures and Algorithms | 3-0-2-5 | CS102 |
| 1 | CS104 | Digital Logic Design | 3-0-2-5 | - |
| 1 | CS105 | Physics for Computer Science | 3-0-2-5 | - |
| 1 | CS106 | English Communication | 3-0-0-3 | - |
| 2 | CS201 | Object-Oriented Programming | 3-0-2-5 | CS102 |
| 2 | CS202 | Database Management Systems | 3-0-2-5 | CS103 |
| 2 | CS203 | Computer Organization | 3-0-2-5 | CS104 |
| 2 | CS204 | Operating Systems | 3-0-2-5 | CS203 |
| 2 | CS205 | Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS206 | Chemistry for Engineers | 3-0-2-5 | - |
| 3 | CS301 | Algorithms | 3-0-2-5 | CS201 |
| 3 | CS302 | Software Engineering | 3-0-2-5 | CS201 |
| 3 | CS303 | Artificial Intelligence | 3-0-2-5 | CS301 |
| 3 | CS304 | Computer Networks | 3-0-2-5 | CS204 |
| 3 | CS305 | Compiler Design | 3-0-2-5 | CS301 |
| 3 | CS306 | Discrete Mathematics | 3-1-0-4 | CS101 |
| 4 | CS401 | Machine Learning | 3-0-2-5 | CS301 |
| 4 | CS402 | Cybersecurity | 3-0-2-5 | CS304 |
| 4 | CS403 | Web Technologies | 3-0-2-5 | CS201 |
| 4 | CS404 | Embedded Systems | 3-0-2-5 | CS203 |
| 4 | CS405 | Human-Computer Interaction | 3-0-2-5 | CS302 |
| 4 | CS406 | Statistics for Data Science | 3-1-0-4 | CS205 |
Detailed Elective Course Descriptions
Departmental electives in the Computer Science program offer students the flexibility to explore specialized areas of interest and align their learning with career goals. Below are descriptions for advanced departmental elective courses:
Advanced Machine Learning
This course delves into supervised, unsupervised, and reinforcement learning techniques, including deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer architectures. Students will gain hands-on experience with frameworks like TensorFlow and PyTorch while working on real-world datasets.
Cryptography and Network Security
This elective explores modern cryptographic algorithms, secure communication protocols, and network defense mechanisms. Topics include symmetric and asymmetric encryption, digital signatures, hash functions, and security policies in distributed systems.
Software Architecture and Design Patterns
Students learn how to design scalable software systems using industry-standard patterns such as MVC, MVP, MVVM, and microservices architecture. The course includes practical sessions on modeling tools like UML and enterprise-level frameworks.
Data Mining and Knowledge Discovery
This course covers data preprocessing, clustering, classification, association rule mining, and anomaly detection. Students use libraries such as scikit-learn and pandas to extract insights from large datasets and build predictive models.
Mobile Application Development
Focuses on developing cross-platform applications using tools like Flutter, React Native, and Xamarin. The course includes both frontend and backend development, with emphasis on user interface design and app deployment strategies.
Internet of Things (IoT) Technologies
Students study IoT architectures, sensor networks, wireless communication protocols, and embedded system programming. Practical labs involve building IoT devices using Raspberry Pi, Arduino, and NodeMCU.
Cloud Computing and DevOps
This course introduces cloud platforms like AWS, Azure, and GCP, along with DevOps practices such as CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and infrastructure automation using Terraform.
Game Development Fundamentals
Explores game design principles, 2D/3D graphics rendering, physics simulation, and interactive storytelling. Students develop games using Unity or Unreal Engine, gaining experience in real-time rendering and user interaction.
Natural Language Processing (NLP)
Focuses on text analysis, language modeling, sentiment analysis, and machine translation techniques. Students implement NLP pipelines using spaCy, NLTK, and transformers-based models for tasks like named entity recognition and question answering.
Quantitative Finance and Risk Modeling
Introduces mathematical models used in financial markets, including option pricing, portfolio optimization, risk management, and derivative valuation. Students apply computational methods to analyze market data and build trading strategies.
Project-Based Learning Philosophy
The Computer Science program at Mahapurusha Srimanta Sankaradeva Viswavidyalaya Nagaon places significant emphasis on project-based learning as a core pedagogical strategy. This approach ensures that students apply theoretical concepts in practical scenarios, fostering critical thinking, collaboration, and innovation.
Mini-projects are introduced from the second year onwards, allowing students to experiment with emerging technologies and solve real-world problems. These projects typically span 3-4 weeks and are assessed through presentations, peer reviews, and documentation.
The final-year thesis/capstone project is a comprehensive endeavor that requires students to work closely with faculty mentors on an original research or development initiative. Students select topics aligned with their interests and career aspirations, often resulting in publications or patent applications.
Project selection involves workshops conducted by the department to guide students in choosing suitable domains and methodologies. Faculty members from different specializations serve as mentors, offering technical guidance and feedback throughout the process. The final evaluation includes oral defense, written report, and demonstration of deliverables.