Pennsylvania State University-Penn State Fayette- Eberly · Courses
SODA
8 courses with the subject SODA, 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.
SODA 110NSocial Data, Technology, and Artificial Intelligence3
SoDA 110N introduces students to the landscape of artificial intelligence (AI) and digital technology through the lens of human-generated data and societal applications. Emphasizing how data is produced and structured before it is ever used in algorithmic systems, the course examines the pipeline from raw human-generated data to AI implementation. Through real-world case studies and hands-on exercises, students develop foundational literacy in data systems and analytics processes, while critically exploring the social, political, and ethical implications of AI. Designed for students from diverse disciplines, the course requires no prior programming experience and fulfills General Education requirements.
SODA 308Research Design for Social Data Analytics3
This course engages students in the study and use of research design tools for the analysis of "big data." SODA 308 Research Design for Social Data Analytics (3) The tools of social science and social data analytics affect how data scientists and social scientists understand the world. This course engages students in the study and use of research design tools for the analysis of social systems and "big data." Topics to be addressed include: how the scientific method relates to a practice of establishing the validity of propositions and the role that analytics can play in that process when the observations are vast and varied; how the validity of systematic patterns in data are assessed as well as how spurious or biased patterns in the data are ruled out; and how the scientific method can guide the use of exploratory techniques such as machine learning and visual analytics. Through the course, students will learn to develop innovative research designs in an effort to improve the statistical analyses used with social data and how to present these analyses to nontechnical audiences, such as non-profits, employers, and the general public. Course requirements include several short memoranda that require the development and presentation of a research design and data analysis plan. Students will also gain practical experience working with several "big data" sets. Students are required to have an understanding of introductory statistics (equivalent to the knowledge they would gain from PL SC 309) prior to taking this course.
SODA 314Social Data and the Methodological Foundations of AI3
This course offers advanced, applied training in the use and understanding of artificial intelligence technologies and their applications to large-scale, human-generated information. Students gain hands-on experience with tools such as open-source generative models, APIs, and agentic systems, learning how these systems function and how to integrate them into research and communication workflows. Alongside technical learning, the course emphasizes critical evaluation of AI applications, ethical risks, and broader consequences for social institutions and human well-being. This integrated approach prepares students to be both capable expert users and thoughtful evaluators of AI.
Interdisciplinary integration of computational, informational, statistical, visual analytic, and social scientific approaches to the creation of big social data. This course addresses computational, informational, statistical, visual analytic, and social scientific approaches to the creation of data that are both "social" (about, or arising from, human interactions) and big (of sufficient scale, variety, or complexity to strain the informational, computational, or cognitive limits of conventional social scientific approaches to data collection or analysis). Examples include text, image, audio, video, intensive spatial and/or longitudinal data, data with complex network, hierarchical and/or other relational information, data from distributed sensors and mobile devices, digitized archival data, and data exhaust from sources like social media. Possible topics include sources of social data, data structures and formats for social data, data collection and manipulation technologies, data linkage and alignment, ethics and scientific responsibility in human subjects research, experimental and observational data collection design for causal inference, measurement of latent social concepts, reliability and validity, search and information retrieval, nonrelational and distributed databases, and standards for data preservation and sharing.
SODA 502Social Data Analytics: Approaches and Issues3
Interdisciplinary integration of computational, informational, statistical, visual analytic, and social scientific approaches to learning from big social data. This course addresses the interdisciplinary integration of computational, informational, statistical, visual analytic, and social scientific approaches to learning from data that are both "social" (about, or arising from, human interactions) and "big" (of sufficient scale, variety, or complexity to strain the informational, computational, or cognitive limits of conventional social scientific approaches to data collection or analysis). Topics include alternative scientific models for learning from data (Bayesian inference, causal inference, statistical / machine learning, visual analytics, measurement modeling), analytics issues with big data (variable selection, parallel computing, algorithmic scaling, ensemble modeling, validation), analytics issues with particular structures and channels of social data (network data, geospatial data, intensive longitudinal data, text data), and issues of scientific responsibility and ethics in analysis of big social data.
