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The Quantitative Methods in the Social Science (QMSS) program aims to train undergraduate students in the theories and methods needed to be successful data literate social scientists. QMSS 301 - Quantitative Social Science Analysis and Big Data: This course will cover methodological approaches to answering social questions that combine theory and skills from social science, social research methodology, and big data techniques. Topics include developing social science questions and identifying, accessing, managing, and analyzing data. Students learn web scraping, geospatial analysis, text-based analysis, and predictive analysis using R and Python.
Job Responsibility:
Attend course lectures (3 hours/week)
Teach 3 x 1-hour lab sections each week
Dedicate at least 2 hours per week of office hours and/or individual meeting times
Attend weekly QMSS Community Hours events (19.5 hours total for semester)
Participate in weekly teaching team meetings
Assist with software/tools/datasets
Co-create problem sets and other assignments
Grade and provide constructive feedback on assignments and projects
Participate in QMSS program activities
Requirements:
Graduate student in good standing
Registered for not less than six (6) credit hours (five with advisor approval)
Proficiency with analytic tools (Excel, Tableau, Stata, R, Python, etc.)
Experience with social sciences research, coursework, and/or perspectives as they pertain to data analysis, interpretation, and communication
Nice to have:
LSA doctoral students within their funding package
Mastery of quantitative methods in the social sciences
Graduate degree in a quantitative methods- or social sciences-related field
Real-world job/internship, teaching, and/or research experience
Strong commitment to serving as a resource for undergraduate students
Ability to make quantitative methods accessible to students of all levels