Advanced Biostatistics
( 25 Modules )

Module #1
Introduction to Advanced Biostatistics
Overview of the course, importance of biostatistics in healthcare, and review of basic statistical concepts

Module #2
Advanced Hypothesis Testing
Review of hypothesis testing, type I and type II errors, and introduction to multiple testing corrections

Module #3
Non-Parametric Tests
Introduction to non-parametric tests, Wilcoxon rank-sum test, and Kruskal-Wallis test

Module #4
Resampling Methods
Introduction to resampling methods, bootstrap, and permutation tests

Module #5
Linear Regression
Review of simple and multiple linear regression, residual analysis, and model diagnostics

Module #6
Generalized Linear Models
Introduction to generalized linear models, logistic regression, and Poisson regression

Module #7
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimator, and Cox proportional hazards model

Module #8
Time-to-Event Data
Analysis of time-to-event data, censoring, and truncation

Module #9
Longitudinal Data Analysis
Introduction to longitudinal data analysis, mixed effects models, and generalized estimating equations

Module #10
Non-Normal Data
Analysis of non-normal data, transformations, and robust methods

Module #11
Correlated Data
Analysis of correlated data, clustered data, and generalized linear mixed models

Module #12
Missing Data
Introduction to missing data, types of missingness, and multiple imputation

Module #13
Meta-Analysis
Introduction to meta-analysis, fixed and random effects models, and forest plots

Module #14
High-Dimensional Data
Introduction to high-dimensional data, feature selection, and dimension reduction techniques

Module #15
Machine Learning in Biostatistics
Introduction to machine learning, supervised and unsupervised learning, and model evaluation

Module #16
Genomics and Proteomics
Introduction to genomics and proteomics, microarray analysis, and RNA-seq analysis

Module #17
Epidemiology and Biostatistics
Introduction to epidemiology, study designs, and measures of disease frequency

Module #18
Clinical Trials
Introduction to clinical trials, phases of clinical trials, and sample size calculation

Module #19
Ethics in Biostatistics
Ethical considerations in biostatistics, informed consent, and data privacy

Module #20
Computational Tools in Biostatistics
Introduction to computational tools in biostatistics, R, Python, and SAS

Module #21
Data Visualization in Biostatistics
Introduction to data visualization, exploratory data analysis, and visualization best practices

Module #22
Statistical Computing
Introduction to statistical computing, simulation, and Monte Carlo methods

Module #23
Big Data in Biostatistics
Introduction to big data, data warehousing, and distributed computing

Module #24
Case Studies in Biostatistics
Real-world case studies in biostatistics, applications, and critical thinking

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Biostatistics career

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Advanced Biostatistics ( 25 Modules )

Advanced Biostatistics
( 25 Modules )