This cluster is ideal for majors in Sociology and Legal Studies.
Course Information
SOCIOL 1 001: Introduction to Sociology, 4 Units
- This course may satisfy prerequisites for the following majors: Sociology, Psychology, Legal Studies, Media Studies, Social Welfare, Interdisciplinary Studies Field
- This course meets Social & Behavioral Sciences, L&S Breadth.
- Schedule: MWF 11:00 AM-12:00 PM; Discussion MW 3:00-4:00 PM
SOCIOL 5 001: Evaluation of Evidence, 4 Units
- This course may satisfy prerequisites for Sociology, Legal Studies.
- This course meets Social & Behavioral Sciences, L&S Breadth.
- Schedule: TR 11:00 AM-12:30 PM; Discussion TR 10:00-11:00 AM
DATA C8 001: Foundations of Data Science, 4 Units
- This course may satisfy prerequisites for the following majors: Sociology, Psychology, Legal Studies, Media Studies, Social Welfare, Environmental Economics and Policy, Public Health, Global Studies.
- Schedule: MWF 10:00-11:00 AM; Discussion R 4:00-6:00 PM
Full Course Information
- SOCIOL 1: Introduction to Sociology, 4 Units
Introduces students who are considering majoring in sociology to the basic topics, concepts, and principles of the study of society. This course is required for the major; 1 or any version of 3 is prerequisite for other sociology classes; students not considering a sociology major are directed to any version of 3 or 3AC. - SOCIOL 5: Evaluation of Evidence, 4 Units
A review of methodological problems in assessing data relating to social life. Topics to be covered include: posing a sociological problem, gaining access to data, measuring, establishing correlation and causal connection among data, and relating data to theoretical context. - DATA C8: Foundations of Data Science, 4 Units
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.