Examines quantitative literacy from a data and evidence driven perspective. Looks at the literature behind vaccines, climate, and other contentious topics where there is a wealth of scientific literature and yet these areas are still hotly debated. Investigates ways in which data science is abused; how to mislead with statistics, and how these problems have created a lack of trust in science. Through class discussions, case studies and exercises, students learn the basics of ethical thinking in science, understand the history of ethical dilemmas in scientific work, and study the distinct challenges associated with ethics in modern data science.
Enroll requirements
Prerequisites: Prerequisite(s): MAT 114 (142), 117, 119, 170, 171, 265, or 270 with C or better OR Visiting University Student
Practice Exams
Test 1
Test 2
Test 3
Test 4
Example Syllabus (this may not be the syllabus used by your instructor, but gives students an idea of the course content and expectations).
Course Coordinator: Laurence Schneider