Biostatistical Methods and Techniques
( 25 Modules )

Module #1
Introduction to Biostatistics
Overview of biostatistics, importance, and applications in healthcare and medicine

Module #2
Descriptive Statistics
Summary statistics, data visualization, and exploratory data analysis

Module #3
Probability Theory
Basic concepts of probability, probability distributions, and Bayes theorem

Module #4
Inferential Statistics
Hypothesis testing, confidence intervals, and p-values

Module #5
Types of Statistical Studies
Observational studies, experimental studies, and quasi-experimental studies

Module #6
Survey Research Methods
Sampling methods, survey design, and data collection techniques

Module #7
Randomized Controlled Trials (RCTs)
Design, implementation, and analysis of RCTs

Module #8
Cohort Studies
Design, analysis, and interpretation of cohort studies

Module #9
Case-Control Studies
Design, analysis, and interpretation of case-control studies

Module #10
Regression Analysis
Simple and multiple linear regression, model assumptions, and diagnostics

Module #11
Logistic Regression
Binary logistic regression, odds ratios, and model interpretation

Module #12
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimate, and Cox proportional hazards model

Module #13
Time Series Analysis
Introduction to time series analysis, autocorrelation, and ARIMA models

Module #14
Non-Parametric Statistics
Wilcoxon rank-sum test, Kruskal-Wallis test, and Sign test

Module #15
Meta-Analysis
Introduction to meta-analysis, fixed-effect and random-effects models

Module #16
Data Visualization
Best practices for data visualization, graph types, and tools

Module #17
Missing Data and Imputation
Types of missing data, reasons for missingness, and imputation techniques

Module #18
Generalized Linear Models (GLMs)
GLMs, model interpretation, and model validation

Module #19
Mixed Effects Models
Introduction to mixed effects models, model estimation, and model interpretation

Module #20
Machine Learning in Biostatistics
Introduction to machine learning, supervised and unsupervised learning

Module #21
Genomic Data Analysis
Introduction to genomic data, RNA-seq analysis, and differential expression analysis

Module #22
Epidemiological Methods
Epidemiological study designs, measures of disease frequency, and bias

Module #23
Clinical Trials Design
Design and conduct of clinical trials, phase I-IV trials, and trial monitoring

Module #24
Biostatistical Computing
Introduction to R, Python, and SAS programming for biostatistical analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Biostatistical Methods and Techniques career

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Biostatistical Methods and Techniques ( 25 Modules )

Biostatistical Methods and Techniques
( 25 Modules )