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Last date of admission: 15th Sep
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Semester 1
Basic Statistical Methods using Excel
This subject introduces the fundamentals of descriptive statistics, data summarization, measures of central tendency and dispersion, correlation analysis, and data visualization using Microsoft Excel.
Probability and Probability Distribution
This subject covers the principles of probability, random variables, mathematical expectation, and discrete and continuous probability distributions for statistical analysis and decision-making.
Programming with R and Python
This subject builds programming skills in R and Python, covering data structures, data manipulation, statistical computing, visualization, and data analysis techniques.
Statistical Inference
This subject introduces statistical inference through sampling distributions, estimation, confidence intervals, hypothesis testing, and parametric and non-parametric methods for data-driven decision-making.
Database Management
This subject covers database concepts, relational database design, SQL, data querying, and techniques for managing and integrating structured datasets.
Semester 2
Linear Regression Models
This subject explores simple and multiple linear regression, model estimation, diagnostics, validation, variable selection, and interpretation for predictive analysis.
Applied Multivariate Data Analysis
This subject introduces multivariate statistical techniques, including principal component analysis, factor analysis, cluster analysis, and discriminant analysis for complex datasets.
Categorical Data Analysis and Generalized Linear Models
This subject focuses on analyzing categorical data using contingency tables, logistic regression, generalized linear models, model diagnostics, and interpretation.
Population Health and Survival Analysis
This subject examines population health measures, survival analysis techniques, life tables, Kaplan-Meier estimation, Cox proportional hazards models, and their healthcare applications.
Design and Analysis of Experiments
This subject introduces the principles of experimental design, randomization, ANOVA, factorial experiments, and statistical analysis for reliable research outcomes.
Semester 3
Machine Learning Methods
This subject introduces supervised and unsupervised machine learning techniques, including classification, regression, clustering, feature selection, and model evaluation for data analysis.
Research Methodology
This subject develops research skills through study design, hypothesis formulation, literature review, research ethics, data collection, statistical planning, and scientific reporting.
Data Visualization using Tableau & Power BI
This subject teaches learners to create interactive dashboards, visual reports, and data-driven insights using Tableau and Power BI.
Sampling and Sample Size Estimation
This subject covers probability and non-probability sampling methods, sampling errors, power analysis, and sample size estimation for effective research design.
Time Series Analysis
This subject introduces time series concepts, forecasting techniques, ARIMA models, trend analysis, and model diagnostics for analyzing time-dependent data.
Semester 4
Bayesian Statistical Modelling
This subject explores Bayesian principles, prior and posterior distributions, hierarchical models, computational methods, and Bayesian inference for statistical modelling.
Longitudinal Data Analysis
This subject focuses on analyzing repeated-measures and longitudinal data using mixed-effects models, generalized estimating equations, and correlation structures.
Demography and Vital Statistics
This subject examines demographic measures, fertility, mortality, migration, population projections, and vital statistics for public health planning and policy.
Capstone Project
This capstone project enables learners to apply statistical, computational, and research methods to solve a real-world biostatistics or healthcare problem through end-to-end project execution.
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