Biostatistics in Public Health
Master the statistical methods essential for public health research, including data analysis, hypothesis testing, and modeling for biological and health-related data.
69 courses
Analyze time-to-event data using R through foundational statistical methods, Cox proportional hazards modeling, and modern machine learning techniques.
Master the core statistical concepts and data analysis methods needed to interpret clinical studies and address real-world public health challenges.
Learn to analyze, visualize, and interpret health data using essential statistical methods and the Tidyverse toolkit.
Learn to calculate, interpret, and present essential biostatistical measures to accurately analyze public health data and scientific literature.
Learn to interpret health data, understand variable distributions, and conduct hypothesis testing to support evidence-based public health research.
Master the fundamentals of statistical inference and hypothesis testing to draw meaningful conclusions from public health data.
Learn to critically evaluate, interpret, and apply infectious disease transmission models to make informed, evidence-based public health policy decisions.
Learn to build and interpret linear regression models using R, enabling you to analyze public health data and uncover insights into health determinants.
Learn how to analyze relationships between health outcomes and predictors using linear regression to make data-driven decisions in public health and biostatistics.
Master the art of analyzing messy health datasets and building predictive models to identify patient risks using the R programming language.
Learn how to analyze time-to-event data, calculate survival rates, and model hazards using R to make data-driven decisions in public health and clinical research.
Learn to analyze complex health data and predict outcomes by mastering multiple regression techniques for biostatistics and evidence-based research.
Learn how to apply artificial intelligence and data-driven frameworks to address critical community health and environmental challenges.
Learn how to collect and analyze population-level data using traditional surveys and emerging digital tools to inform public health policy.
Learn to analyze healthcare data and perform essential statistical tests using SPSS to support clinical research and medical decision-making.
Master the essential science, communication techniques, and ethical protocols required to work as a contact tracer and support public health.
Master the core principles of hypothesis testing, confidence intervals, and data analysis using one and two-sample statistical methods.
Learn to analyze public datasets and inform government decisions using R programming and modern data science workflows.
Learn how to leverage population-level data to design effective health programs and drive evidence-based decisions in public health.
Learn to transform evaluation plans into structured analysis, interpret quantitative survey data, and draw actionable insights for health and social programs.
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