5 courses with the subject DATA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DATA 0001Applied AI: A Practical Introduction
This course introduces students to artificial intelligence as a practical professional skill. Students develop the critical fluency needed to use AI tools effectively, evaluate their outputs accurately, and apply them responsibly across a range of workplace contexts. Topics include how AI systems work, the nature and limits of large language models, bias and framing in AI outputs, and AI's implications for work and society. No technical or statistical background is required. 1 Course Unit
In Data Analytics 2100: Intermediate Data Analytics students learn the fundamentals of two skills required by many data science jobs: survey and experimental research. The course trains students in all aspects of the survey research process, including designing good survey questionnaires, drawing samples, weighting data, and analyzing survey responses. Students come away from the class with an understanding in how to design, analyze a randomized experiment and build upon the R skills gained in previous courses. Certificate students and individual course takers must complete a prerequisite data analytics course before enrolling in this course. Although courses in the Certificate in Data Analytics must be taken sequentially to build your expertise in data analytics, you have the option to take courses in order without committing to the entire certificate. Students who complete all four courses earn the Certificate in Data Analytics. Please submit a permission request in Path@Penn to register for this class. 1 Course Unit
Introduction to Statistical Methods exposes students to the process by which quantitative social science and data science research is conducted. The class revolves around three separate, but related tracks. Track one teaches some basic tools necessary to conduct quantitative social science research. Topics covered include descriptive statistics, sampling, probability, and statistical theory. Track two teaches students how to implement these basic tools using R. The third track teaches students the fundamentals of research design. Topics will include independent and dependent variables, generating testable hypotheses, and issues in causality. Please submit a permission request in Path@Penn to register for this class. 1 Course Unit
