Comprehensive Course Structure
The Biostatistics program at IIPHG is structured over eight semesters, with a carefully designed progression from foundational courses to advanced specializations. Each semester includes core courses, departmental electives, science electives, and laboratory sessions to ensure a well-rounded educational experience.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-1-0-4 | None |
| 1 | MATH102 | Linear Algebra | 3-1-0-4 | None |
| 1 | BIO101 | Introduction to Biology | 3-1-0-4 | None |
| 1 | STAT101 | Introduction to Statistics | 3-1-0-4 | None |
| 1 | CS101 | Programming Fundamentals | 3-1-0-4 | None |
| 2 | MATH201 | Calculus II | 3-1-0-4 | MATH101 |
| 2 | MATH202 | Probability Theory | 3-1-0-4 | MATH101 |
| 2 | BIO201 | Cell Biology | 3-1-0-4 | BIO101 |
| 2 | STAT201 | Statistical Inference | 3-1-0-4 | STAT101 |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| 3 | MATH301 | Advanced Calculus | 3-1-0-4 | MATH201 |
| 3 | STAT301 | Regression Analysis | 3-1-0-4 | STAT201 |
| 3 | BIO301 | Molecular Biology | 3-1-0-4 | BIO201 |
| 3 | STAT302 | Experimental Design | 3-1-0-4 | STAT201 |
| 3 | CS301 | Database Systems | 3-1-0-4 | CS201 |
| 4 | MATH401 | Differential Equations | 3-1-0-4 | MATH301 |
| 4 | STAT401 | Bioinformatics | 3-1-0-4 | STAT301 |
| 4 | BIO401 | Genetics and Genomics | 3-1-0-4 | BIO301 |
| 4 | STAT402 | Survival Analysis | 3-1-0-4 | STAT301 |
| 4 | CS401 | Machine Learning | 3-1-0-4 | CS301 |
| 5 | STAT501 | Bayesian Methods | 3-1-0-4 | STAT401 |
| 5 | BIO501 | Environmental Health | 3-1-0-4 | BIO401 |
| 5 | ECON501 | Health Economics | 3-1-0-4 | STAT401 |
| 5 | STAT502 | Clinical Trials Design | 3-1-0-4 | STAT402 |
| 5 | CS501 | Advanced Data Visualization | 3-1-0-4 | CS401 |
| 6 | STAT601 | Multivariate Analysis | 3-1-0-4 | STAT501 |
| 6 | BIO601 | Systems Biology | 3-1-0-4 | BIO501 |
| 6 | ECON601 | Health Policy and Planning | 3-1-0-4 | ECON501 |
| 6 | STAT602 | Time Series Analysis | 3-1-0-4 | STAT502 |
| 6 | CS601 | Deep Learning | 3-1-0-4 | CS501 |
| 7 | STAT701 | Advanced Statistical Modeling | 3-1-0-4 | STAT601 |
| 7 | BIO701 | Global Health Challenges | 3-1-0-4 | BIO601 |
| 7 | ECON701 | Global Health Economics | 3-1-0-4 | ECON601 |
| 7 | STAT702 | Risk Assessment and Management | 3-1-0-4 | STAT602 |
| 7 | CS701 | Data Mining and Big Data Analytics | 3-1-0-4 | CS601 |
| 8 | STAT801 | Capstone Project in Biostatistics | 3-0-0-6 | All previous courses |
Detailed Departmental Elective Courses
The following advanced departmental elective courses are offered in the Biostatistics program:
1. Bayesian Methods for Health Data
This course introduces students to Bayesian statistical modeling techniques applied to health-related data. Students learn how to construct prior distributions, perform posterior inference, and interpret results using real-world datasets from clinical trials and epidemiological studies.
2. Clinical Trials Design and Analysis
This course covers the principles of designing and analyzing clinical trials in pharmaceutical and biotechnology industries. Students explore various trial designs, including phase I-IV trials, adaptive designs, and regulatory considerations for drug development.
3. Genomic Data Analysis
This advanced course focuses on statistical methods for analyzing large-scale genomic data. Topics include genome-wide association studies (GWAS), next-generation sequencing data analysis, and integration of multi-omics datasets using statistical tools.
4. Environmental Health Statistics
Students study statistical methods used to analyze environmental exposures and their impact on health outcomes. The course covers exposure assessment techniques, risk modeling, and policy applications in addressing climate change and pollution-related health issues.
5. Machine Learning for Healthcare Applications
This course explores how machine learning algorithms can be applied to healthcare problems such as disease prediction, diagnosis support, and treatment optimization. Students implement models using Python libraries and evaluate their performance on real clinical datasets.
6. Health Economics and Outcomes Research
This course teaches students how to conduct economic evaluations of health interventions and assess their impact on population health outcomes. Topics include cost-effectiveness analysis, budget impact modeling, and value-based healthcare delivery systems.
7. Multivariate Statistical Methods
Students learn advanced multivariate techniques for analyzing complex datasets with multiple variables. The course covers principal component analysis, factor analysis, cluster analysis, and other dimensionality reduction methods used in health data analysis.
8. Survival Analysis and Time-to-Event Data
This course focuses on statistical methods for analyzing time-to-event data, commonly encountered in clinical research and public health studies. Students learn about Kaplan-Meier estimation, Cox proportional hazards models, and competing risks analysis.
9. Bioinformatics and Computational Biology
This interdisciplinary course combines statistical theory with computational biology to analyze biological systems at the molecular level. Students gain hands-on experience with bioinformatics tools and databases for sequence analysis, protein structure prediction, and gene expression profiling.
10. Global Health Analytics
This course examines statistical approaches for analyzing global health disparities and evaluating interventions aimed at improving health outcomes in low-resource settings. Students work with real datasets from international organizations to understand the challenges and opportunities in global health research.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes the integration of theoretical knowledge with practical applications through hands-on research experiences. This approach fosters critical thinking, collaboration, and innovation while preparing students for real-world challenges.
Mini-projects are introduced in the second year, allowing students to apply fundamental concepts learned in earlier semesters to real datasets. These projects typically last 6-8 weeks and involve working in small teams under faculty supervision. Students present their findings in both written reports and oral presentations, receiving feedback from peers and instructors.
The final-year thesis/capstone project is a significant component of the program that requires students to conduct original research under the guidance of a faculty mentor. This culminating experience allows students to demonstrate mastery of both statistical theory and practical application in addressing complex health-related problems. Students are encouraged to collaborate with external partners, including industry sponsors or public health organizations, to ensure relevance and impact.
Project selection involves a competitive process where students propose research topics aligned with their interests and career goals. Faculty mentors are matched based on expertise and availability, ensuring that each student receives personalized guidance throughout the research process. The evaluation criteria include methodology, data analysis, presentation quality, and contribution to the field of biostatistics.