The modern world is awash with data, and making sense of it requires specialized skills. This course will expose students to commonly used data analytics techniques. Topics include the acquisition, manipulation, and transformation of structured data, exploratory data analysis, data visualization, and predictive modeling. Students in this course will learn and use the Python programming language and tools for working with data. Analysis will be performed using real data sets. Does not count as a CS Elective (BA or BS).

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

This course is an introduction to the concepts and skills involved with the collection, management, analysis, and presentation of data sets and the data products that result from the work of data scientists. Privacy, algorithmic bias and ethical issues are also discussed. Students will work with data from the financial, epidemiological, educational, and other domains. The course provides many examples of real-world data that students work with using various software tools.

This course is an introduction to the concepts and skills involved with the collection, management, analysis, and presentation of data sets and the data products that result from the work of data scientists. Privacy, algorithmic bias and ethical issues are also discussed. Students will work with data from the financial, epidemiological, educational, and other domains. The course provides many examples of real-world data that students work with using various software tools.

The course will cover the technical foundations and use of today's communication networks, particularly the internet. It will also address key social, policy, economic, and legal aspects of these networks, their use (and abuse) and their regulation. This course covers computer science topics, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. This course is not intended for Computer Science majors or minors; students interested with a major or minor-level treatment of this material should see COMPSCI 453.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

Mathematical techniques useful in the study of computing and information processing. The mathematical method of definition and proof. Sets, functions, and relations. Combinatorics, probability and probabilistic reasoning. Graphs and trees as models of data and of computational processes. Prerequisite: R1 math skills recommended. Not intended for Computer Science majors and Math (Statistics) majors - students interested in a majors-level treatment of this material should see COMPSCI 240 and 250, or MATH 455.