Certificate in Biostatistics for Predictive Models
-- ViewingNowThe Certificate in Biostatistics for Predictive Models is a comprehensive course designed to equip learners with essential skills in biostatistics, a vital discipline in healthcare, public health, and medical research. This program covers key topics including regression analysis, clinical trial design, and predictive modeling, providing a strong foundation for data-driven decision making.
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โข Introduction to Biostatistics: Basic concepts, types of data, and data collection methods. Descriptive and inferential statistics, probability, and hypothesis testing.
โข Data Analysis and Visualization: Data cleaning and preprocessing, exploratory data analysis, data visualization techniques, and using R or Python for data analysis.
โข Regression Analysis: Simple and multiple linear regression, logistic regression, and polynomial regression. Assumptions and diagnostics of regression models.
โข Generalized Linear Models (GLMs): Introduction to GLMs, link functions, and estimation methods. Applications to count data, binary data, and continuous data with non-normal distributions.
โข Survival Analysis: Survival functions, hazard functions, Kaplan-Meier estimates, and Cox proportional hazards models. Applications in medical research and public health.
โข Machine Learning for Predictive Modeling: Overview of machine learning algorithms, including decision trees, random forests, and neural networks. Model evaluation, cross-validation, and overfitting.
โข Time Series Analysis: Time series components, autocorrelation and partial autocorrelation functions, seasonal decomposition, ARIMA models, and forecasting.
โข Multivariate Analysis: Principal component analysis, factor analysis, cluster analysis, and discriminant analysis. Applications in public health research and epidemiology.
โข Causal Inference: Potential outcomes framework, propensity score matching, inverse probability weighting, and regression adjustment. Applications in public health interventions and evaluations.
Note: The above list of units can vary depending on the specific program requirements and the target audience.
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