An individually arranged Phase 3 extramural elective in preventive medicine, engaging students in primary or specialty patient care in ambulatory and/or hospital settings outside the UW Madison Statewide Campus.
An individually arranged Phase 3 extramural elective in anesthesiology where students provide primary or specialty patient care in ambulatory and/or hospital settings.
An individually arranged Phase 3 extramural elective in Human Oncology where students engage in primary or specialty patient care in ambulatory and/or hospital settings outside the UW Madison Statewide Campus.
Extramural Elective-Medicine is an individually arranged Phase 3 elective outside the UW Madison Statewide Campus, focusing on primary or specialty patient care in ambulatory and/or hospital settings.
Individually arranged Phase 3 extramural elective in Obstetrics and Gynecology involving primary or specialty patient care in ambulatory and/or hospital settings.
Individually arranged Phase 3 elective outside UW Madison Statewide Campus to provide primary or specialty patient care in ambulatory and/or hospital settings.
An individually arranged Phase 3 extramural elective in pediatrics where students engage in primary or specialty patient care in ambulatory and/or hospital settings outside the UW Madison Statewide Campus.
Individually arranged Phase 3 psychiatry elective outside UW Madison Statewide Campus, engaging in primary or specialty patient care in ambulatory and/or hospital settings.
An individually arranged Phase 3 extramural elective for surgery, focusing on providing primary or specialty patient care in ambulatory or hospital settings.
Individually arranged Phase 3 extramural elective for providing primary or specialty patient care in ambulatory or hospital settings outside the UW Madison Statewide Campus.
Extramural elective clerkship in international health where students engage in primary or specialty patient care in ambulatory/hospital settings, potentially including community public health, prevention, and health education.
Seminar and projects course introducing health disparities in Milwaukee and community and public health improvement projects within the TRUIMPH program.
An advanced seminar for fourth-year medical students continuing TRIUMPH, focusing on exploring health disparities and completing a community or public health improvement project in Milwaukee.
An introductory course covering distributions, measures of central tendency, hypothesis testing, confidence intervals, regression, correlation, and experimental design.
Alexander Covington assigned long weekly homework sets that carried heavy weight in the final grade, while expecting deeper knowledge of content barely covered in lectures.
Past students reported extremely high workloads, noting that homework required significant self-learning because lecture videos were basic. Exams covered extensive details from self-study materials, and solutions were not provided.
R For Statistics II covers the statistical language R, focusing on writing conditional expressions, loops, and functions, manipulating data structures, extracting text data, and creating visualizations.
R FOR STATISTICS III provides an understanding of the statistical language R and teaches combining it with high performance computing tools for scientific computing.
Introduction to mathematical statistics covering probability, discrete distributions, acceptance sampling, normal distribution, central limit theorem, and estimation methods.
Workload was generally manageable, with some students finding homework tougher than exams. Exams were sometimes described as difficult or tricky, but often aligned with practice materials or were curved.
Introduction to Theory and Methods of Mathematical Statistics II covers unbiased estimation, MLE, confidence intervals, hypothesis tests, Neyman-Pearson lemma, LRT, regression, and ANOVA.
The grade was based on about five homework assignments and three exams. One student found the material not difficult but noted it was taught extremely slowly.
Introduction to Statistics for Science and Engineering covers descriptive stats, probability, hypothesis testing, linear regression, ANOVA, and experimental design using R.
Applied Regression Analysis covers linear regression for prediction, feature selection, and interpretation, extending to mixed and generalized linear models using R.
Data Science Modeling II teaches exploring, modeling, and analyzing data using R, covering probability, hypothesis testing, regression, ANOVA, and machine learning methods like random forests.
Some students found the tests very difficult, with one noting they were harder than the midterms. Another described the instructor as super strict, warning students to be careful about making mistakes.