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Develop foundational understanding of common statistical methods in healthcare data analysis, including data wrangling, univariate and multivariate techniques, and practical application using statistical software.
Explore advanced healthcare data analysis using R, covering causal inference, model specification, matching, fixed effects, random effects, longitudinal data, and bootstrapping for informed decision-making.
Learn advanced multivariate statistical methods for healthcare data analysis, including non-linear trends, interacting variables, logistic regression, and its variants. Practice applying these methods to real-world healthcare examples.
Develop practical skills in analyzing healthcare data using R, focusing on linear relationships, correlation, regression, and diagnostic tests for improved decision-making in the field.
Discover how AI and machine learning revolutionize healthcare through real-world applications, ethical considerations, and practical case studies. Master fundamental concepts while exploring current trends and cutting-edge research in healthcare technolo…
Dive into advanced AI and machine learning applications in healthcare, exploring sophisticated techniques, implementation strategies, and emerging trends while working with real-world medical scenarios.
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