Search and navigate to colleges and courses
Apply
Scholarships & exams
Fees
N/A
Placement
93.5%
Avg Package
₹4,20,000
Highest Package
₹8,50,000
Fees
N/A
Placement
93.5%
Avg Package
₹4,20,000
Highest Package
₹8,50,000
Seats
120
Students
120
Seats
120
Students
120
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | BIOS101 | Introduction to Human Biology | 3-0-0-3 | - |
| PHYS101 | Basic Physics for Health Sciences | 3-0-0-3 | - | |
| CHEM101 | Chemistry for Life Sciences | 3-0-0-3 | - | |
| MATH101 | Calculus and Linear Algebra | 4-0-0-4 | - | |
| COMP101 | Computer Fundamentals | 3-0-2-4 | - | |
| STAT101 | Statistics for Health Sciences | 3-0-0-3 | - | |
| ENGL101 | English for Academic Purposes | 2-0-0-2 | - | |
| PHYS102 | Biology Lab Practical | 0-0-4-2 | BIOS101 | |
| MATH102 | Probability and Statistics Lab | 0-0-4-2 | STAT101 | |
| COMP102 | Programming Fundamentals | 0-0-6-3 | COMP101 | |
| 2 | BIOS201 | Molecular Biology | 3-0-0-3 | BIOS101 |
| CHEM201 | Organic Chemistry | 3-0-0-3 | CHEM101 | |
| MATH201 | Advanced Calculus and Differential Equations | 4-0-0-4 | MATH101 | |
| COMP201 | Data Structures and Algorithms | 3-0-2-5 | COMP101 | |
| STAT201 | Biostatistics | 3-0-0-3 | STAT101 | |
| HEAL201 | Introduction to Healthcare Systems | 3-0-0-3 | - | |
| ENGL201 | Technical Writing and Communication | 2-0-0-2 | ENGL101 | |
| COMP202 | Database Management Systems | 3-0-2-5 | COMP101 | |
| MATH202 | Matrix Algebra Lab | 0-0-4-2 | MATH101 | |
| COMP203 | Python Programming Lab | 0-0-6-3 | COMP102 | |
| 3 | BIOS301 | Cellular and Molecular Pathology | 3-0-0-3 | BIOS201 |
| COMP301 | Object-Oriented Programming with Java | 3-0-2-5 | COMP201 | |
| STAT301 | Advanced Biostatistics | 3-0-0-3 | STAT201 | |
| HEAL301 | Healthcare Data Management | 3-0-0-3 | HEAL201 | |
| COMP302 | Machine Learning Fundamentals | 3-0-2-5 | COMP201 | |
| ENGL301 | Research Methodology | 2-0-0-2 | ENGL201 | |
| COMP303 | Web Development Technologies | 3-0-2-5 | COMP201 | |
| MATH301 | Probability and Random Processes | 4-0-0-4 | MATH201 | |
| HEAL302 | Public Health Principles | 3-0-0-3 | HEAL201 | |
| COMP304 | Software Engineering Concepts | 3-0-2-5 | COMP201 | |
| 4 | BIOS401 | Genetics and Genomics | 3-0-0-3 | BIOS301 |
| COMP401 | Advanced Machine Learning | 3-0-2-5 | COMP302 | |
| STAT401 | Clinical Trial Design and Analysis | 3-0-0-3 | STAT301 | |
| HEAL401 | Health Information Systems | 3-0-0-3 | HEAL301 | |
| COMP402 | Data Visualization and Reporting | 3-0-2-5 | COMP301 | |
| ENGL401 | Academic Writing and Presentation Skills | 2-0-0-2 | ENGL301 | |
| COMP403 | Cybersecurity in Healthcare | 3-0-2-5 | COMP301 | |
| MATH401 | Mathematical Modeling in Medicine | 4-0-0-4 | MATH301 | |
| HEAL402 | Ethics and Regulation in Health Informatics | 3-0-0-3 | HEAL301 | |
| COMP404 | Capstone Project Planning | 3-0-2-5 | - | |
| 5 | COMP501 | Natural Language Processing for Healthcare | 3-0-2-5 | COMP401 |
| STAT501 | Survival Analysis and Event Modeling | 3-0-0-3 | STAT401 | |
| HEAL501 | Clinical Decision Support Systems | 3-0-0-3 | HEAL401 | |
| COMP502 | Deep Learning for Medical Imaging | 3-0-2-5 | COMP401 | |
| ENGL501 | Professional Communication in Healthcare | 2-0-0-2 | ENGL401 | |
| COMP503 | Healthcare API Development | 3-0-2-5 | COMP403 | |
| MATH501 | Stochastic Processes in Medicine | 4-0-0-4 | MATH401 | |
| HEAL502 | Healthcare Analytics Capstone | 3-0-0-3 | HEAL402 | |
| COMP504 | Privacy by Design in Healthcare | 3-0-2-5 | COMP403 | |
| BIOS501 | Advanced Genomics and Proteomics | 3-0-0-3 | BIOS401 | |
| 6 | COMP601 | Digital Therapeutics Development | 3-0-2-5 | COMP501 |
| STAT601 | Epidemiology and Disease Surveillance | 3-0-0-3 | STAT501 | |
| HEAL601 | Healthcare Policy and Reform | 3-0-0-3 | HEAL501 | |
| COMP602 | Mobile Health Applications | 3-0-2-5 | COMP502 | |
| ENGL601 | Leadership in Healthcare Informatics | 2-0-0-2 | ENGL501 | |
| COMP603 | Healthcare System Optimization | 3-0-2-5 | COMP503 | |
| MATH601 | Computational Modeling in Biology | 4-0-0-4 | MATH501 | |
| HEAL602 | Global Health Informatics | 3-0-0-3 | HEAL502 | |
| COMP604 | Research Ethics in Healthcare Data | 3-0-2-5 | COMP504 | |
| BIOS601 | Systems Biology and Network Analysis | 3-0-0-3 | BIOS501 | |
| 7 | COMP701 | Advanced AI in Clinical Decision Making | 3-0-2-5 | COMP601 |
| STAT701 | Machine Learning for Biomedical Research | 3-0-0-3 | STAT601 | |
| HEAL701 | Healthcare Innovation and Entrepreneurship | 3-0-0-3 | HEAL601 | |
| COMP702 | Blockchain in Healthcare | 3-0-2-5 | COMP602 | |
| ENGL701 | Presentation and Teaching Skills | 2-0-0-2 | ENGL601 | |
| COMP703 | Healthcare Data Visualization Tools | 3-0-2-5 | COMP603 | |
| MATH701 | Mathematical Analysis of Health Data | 4-0-0-4 | MATH601 | |
| HEAL702 | Public Health Surveillance Systems | 3-0-0-3 | HEAL602 | |
| COMP704 | Interdisciplinary Research Methods | 3-0-2-5 | COMP604 | |
| BIOS701 | Emerging Trends in Genomic Medicine | 3-0-0-3 | BIOS601 | |
| 8 | COMP801 | Final Year Thesis Research | 0-0-12-12 | - |
| STAT801 | Advanced Statistical Modeling in Health | 3-0-0-3 | STAT701 | |
| HEAL801 | Capstone Project Implementation | 3-0-0-3 | HEAL701 | |
| COMP802 | Industry Internship and Practical Application | 0-0-12-12 | - | |
| ENGL801 | Final Presentation and Defense | 2-0-0-2 | ENGL701 | |
| COMP803 | Professional Portfolio Development | 3-0-2-5 | COMP703 | |
| MATH801 | Mathematical Foundations of Data Science | 4-0-0-4 | MATH701 | |
| HEAL802 | Graduate School Preparation and Career Counseling | 3-0-0-3 | HEAL702 | |
| COMP804 | Capstone Project Final Review | 3-0-2-5 | COMP801 | |
| BIOS801 | Research Ethics and Responsible Conduct | 3-0-0-3 | BIOS701 |
The department offers a wide array of advanced elective courses designed to provide depth and specialization in various aspects of health informatics. These courses are structured to be both academically rigorous and practically relevant, ensuring that students gain hands-on experience with current tools and methodologies used in the field.
This elective course delves into how natural language processing (NLP) techniques can be applied to extract meaningful insights from unstructured clinical data such as physician notes, patient narratives, and medical literature. Students learn about tokenization, named entity recognition, sentiment analysis, and topic modeling tailored for healthcare contexts. The course includes a project component where students build an NLP pipeline for processing electronic health records and extracting diagnostic information.
This advanced course explores the application of deep learning models to medical image analysis, including radiology, pathology, and ophthalmology. Students study convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures specifically adapted for medical imaging tasks. The course includes laboratory sessions on image preprocessing, model training, and validation using real-world datasets from hospitals and research institutions.
This elective introduces students to the design, implementation, and evaluation of clinical decision support systems (CDSS). Topics include rule-based reasoning, machine learning models for diagnosis prediction, integration with EHR systems, and user interface design. Students work on developing a CDSS prototype using case studies from actual healthcare settings, focusing on improving patient outcomes while maintaining data privacy and ethical standards.
This course focuses on implementing robust privacy measures throughout the entire lifecycle of health information systems. It covers topics such as differential privacy, homomorphic encryption, secure multi-party computation, and compliance with regulations like HIPAA and GDPR. Students gain practical experience through simulations of real-world scenarios involving data breaches, system audits, and regulatory investigations.
This elective explores the development and deployment of mobile health applications for chronic disease management, remote monitoring, and patient engagement. Students learn about mobile app architecture, user experience design, integration with wearable devices, and data security considerations. The course includes a capstone project where students develop a functional mobile application addressing a specific healthcare challenge.
This advanced course covers the development of APIs for connecting various components of healthcare systems. Students learn about RESTful services, GraphQL, OAuth2 authentication, and data interoperability standards such as FHIR (Fast Healthcare Interoperability Resources). The curriculum includes building real-world APIs that integrate with EHR systems, laboratory information systems, and patient portals.
This course explores how blockchain technology can enhance transparency, security, and traceability in healthcare systems. Topics include smart contracts, decentralized identity management, supply chain tracking, and data sharing protocols. Students engage in hands-on exercises to create a blockchain-based solution for managing patient consent or tracking pharmaceutical products through the supply chain.
This elective focuses on improving efficiency and quality in healthcare delivery systems using analytical tools and optimization techniques. Students study queuing theory, resource allocation models, process improvement methodologies like Lean Six Sigma, and performance measurement frameworks. The course includes case studies from leading hospitals and health systems worldwide.
This course addresses global health challenges through the lens of digital health solutions. Students examine health disparities across different regions, cultural considerations in technology adoption, and international policy frameworks governing health data sharing. The curriculum includes projects that involve collaboration with organizations working in low-resource settings to develop scalable health informatics solutions.
This advanced course provides an in-depth exploration of genomics and proteomics data analysis techniques, including genome assembly, variant calling, gene expression profiling, and protein structure prediction. Students gain proficiency in tools like BLAST, GATK, and Galaxy, and apply these skills to research projects involving large-scale genomic datasets.
This elective introduces students to systems biology approaches for understanding complex biological networks and pathways. Topics include gene regulatory networks, protein-protein interaction mapping, pathway enrichment analysis, and network-based drug discovery. Students learn to use software tools like Cytoscape and STRING for visualizing and analyzing biological networks.
This course examines the latest developments in genomic medicine, including precision medicine, pharmacogenomics, germline variant analysis, and clinical implementation of genomic testing. Students stay updated with recent discoveries and regulatory changes affecting genomic medicine practice through literature reviews, expert guest lectures, and interactive discussions.
This course prepares students to identify opportunities for innovation in healthcare and translate ideas into viable business ventures. Topics include ideation techniques, business model canvases, intellectual property protection, venture funding strategies, and regulatory compliance in healthcare startups. Students work on developing a business plan for a healthcare startup idea during the final project component.
This elective focuses on creating effective visual representations of complex health data using modern visualization tools and platforms. Students learn about interactive dashboards, real-time data streaming, geospatial mapping, and storytelling with data. The course includes practical sessions using tools like Tableau, Power BI, and D3.js to create compelling visualizations for healthcare stakeholders.
This course teaches students how to conduct research that bridges multiple disciplines within health informatics. It covers cross-functional collaboration strategies, mixed-methods research designs, qualitative data analysis techniques, and ethical considerations in interdisciplinary projects. Students participate in collaborative research projects involving faculty from diverse departments.
The department's approach to project-based learning is rooted in the belief that meaningful education occurs when students actively engage with real-world problems and develop solutions through hands-on experience. This pedagogical model emphasizes collaboration, critical thinking, and the application of theoretical knowledge to practical challenges.
Mini-projects are integrated into the curriculum from the second year onwards, allowing students to apply foundational concepts learned in class to real-world scenarios. These projects typically span 6-8 weeks and involve working in small teams under faculty supervision. Each project is designed to address a specific challenge within health informatics, such as developing a prototype for a mobile health app, analyzing clinical data for quality improvement initiatives, or designing a privacy-preserving data sharing system.
The final-year thesis or capstone project is the culmination of students' learning journey and serves as their opportunity to demonstrate mastery in health informatics. Projects are selected based on student interests, faculty expertise, and current industry needs. Students work closely with a faculty advisor throughout the process, conducting literature reviews, designing experiments, collecting and analyzing data, and presenting findings at departmental symposiums and conferences.
Students begin selecting their final-year projects in the seventh semester, often after consulting with faculty mentors who specialize in areas aligned with their interests. The selection process involves proposal submissions, peer reviews, and faculty guidance to ensure that each project is feasible, impactful, and aligned with academic standards. Faculty mentors play a crucial role in guiding students through research methodologies, troubleshooting challenges, and preparing them for professional presentations.
Projects are evaluated based on multiple criteria including technical depth, innovation, clarity of communication, adherence to ethical guidelines, and overall impact potential. Peer evaluations, faculty feedback, and external reviewers may be involved in the assessment process. The final grade reflects not only the quality of deliverables but also the student's ability to work collaboratively, manage time effectively, and adapt to evolving project requirements.
