260.705.81
Fundamentals of Quantitative Reasoning in the Biomedical and Health Sciences
Location
Internet
Term
3rd Term
Department
Molecular Microbiology and Immunology
Credit(s)
3
Academic Year
2023 - 2024
Instruction Method
Asynchronous Online
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
none
Are you tired of poor argumentation in the news, in public debates or in even in scientific-professional contexts? Are you interested in learning a structured approach on how to be the most reasonable person in the room using practical logic? Do you enjoy analyzing and evaluating evidence to test hypotheses, and using them to engage in debates about the most pressing, global problems in science and society today? Then join us!
Provides a broad introduction to interdisciplinary, scientific reasoning using current problems from science and society. Explores the fundamentals of basic probability and statistics using real-world datasets from a variety of basic science disciplines. Introduces data analysis and visualization in the natural and biomedical sciences. Explains the importance of computational and quantitative methods for hypothesis testing in science, technology, and daily life.
Learning Objectives
Upon successfully completing this course, students will be able to:
- State the relevance and use of models and hypotheses in research
- Explain the basics of hypothesis testing and data analysis in the life and biomedical sciences
- Employ suitable techniques for visualization or biomedical and health data
- Apply fundamental logic and ethics considerations to observational and experimental approaches to study current problems in science and society
- Practice rational argumentation and convincing yet truthful communication skills in interdisciplinary and public settings
Methods of Assessment
This course is evaluated as follows:
- 30% Problem sets
- 20% Discussion Board
- 30% Final Paper
- 20% Peer-feedback
This course is part of the JHSPH R3 Graduate Science Program.