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In this course students will learn to use social science statistics to test their own research questions, while becoming more educated consumers of statistical analyses presented in research and news sources. Students will employ various inferential statistics techniques commonly used in social science, such as confidence intervals, t-tests, chi-square testing, correlation, ANOVA, and regression. Students will manage and analyze data using the Stata statistical software package.

For students doing work-study or internships, we will focus on three core areas of professionalization. First, each week will journal our work weeks, discussing and sharing our work experiences in a round-table. Second, we will build our professionalization skills, especially networking (in person and on LinkedIn), resume writing, and doing practice interviews. Finally, we will work on writing 5-year plans, to help us figure out where we’d like to be a few years after graduation. More specifically

How do we organize data to solve complex problems efficiently? This course studies the fundamental structures and algorithms that form the cornerstone of computational problem-solving. Building upon the programming foundations established in CS1, we will explore how algorithmic thinking and sophisticated data organization enables us to tackle increasingly challenging computational problems.