This intermediate-level biostatistics course provides students with advanced analytical skills to examine complex health data using regression, prediction modeling, and survival analysis. Students will explore multiple and logistic regression, Poisson models, machine learning algorithms, and time-to-event methods such as Kaplan-Meier and Cox proportional hazards models.
Emphasis is placed on hands-on data analysis using statistical software, the interpretation of results, and real-world applications in public health research. Students will complete weekly micro-projects and statistical lab assignments to strengthen their ability to clean, analyze, and interpret biostatistical data.
By the end of this course, students will be equipped to apply rigorous statistical methods to public health challenges and contribute meaningfully to data-driven decision-making in health research.
Emphasis is placed on hands-on data analysis using statistical software, the interpretation of results, and real-world applications in public health research. Students will complete weekly micro-projects and statistical lab assignments to strengthen their ability to clean, analyze, and interpret biostatistical data.
By the end of this course, students will be equipped to apply rigorous statistical methods to public health challenges and contribute meaningfully to data-driven decision-making in health research.
- Teacher: Tia Warrick
JC_SEMESTER: 25 SU