A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course.

This course will cover advanced specialized topics data science. Such topics are may be emerging and advanced. Specific prerequisites will be noted for each specific special topic.

Hallmarks of AI are systems that perform human-like behaviors, and AI systems rely on continuous preparation and deployment of data resources as new tasks emerge. In this course, students develop their conceptual, applied, and critical understanding about (1) experimental principles and methods guiding the collection, validation, and deployment of human data resources for AI systems; (2) human-centered AI concepts and techniques including dataset bias, debiasing, AI fairness, humans-in-the loop methods, explainable AI, trust), and (3) best practices for technical writing and presentation about AI. As a milestone, based on research review, students will write and present an experimental design proposal for dataset elicitation followed by computational experimentation, with description and visualization of the intended experiment setup, as well as critical reflection of benefits, limitations, and implications in the context of AI system development and deployment.

Concepts and techniques for testing soft ware and assuring its quality. Topics cover software testing at the unit and system levels; static vs. dynamic analysis; functional testing; inspections; and reliability assessment.

The first course in a two-course, senior-level, capstone project experience. Students work as part of a team to develop solutions to problems posed by either internal or external customers. Problems may require considerable software development or evolution and maintenance of existing software products. Culminates with the completion and presentation of the first major increment of the project solution. Students must have co-op completed to enroll.

This is the second course in a two-course, senior-level capstone project experience. Students submit one or more additional increments that build upon the solution submitted at the end of the first course. Students make major presentations for both customers as well as technical-oriented audiences, turn over a complete portfolio of project-related artifacts and offer an evaluation of the project and team experience.

This course provides the student with an opportunity to explore a project-based research experience that advances knowledge in that area. The student selects a research problem, conducts background research, develops the system, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include an in-depth research report on a topic selected by the student and in agreement with the student's adviser. The report must be structured as a conference paper, and must be submitted to a conference selected by the student and his/her adviser.