Data Science

Master of Science

This course explores ethical challenges stemming from data-driven decision making in society. Students will focus on important topics like bias, fairness, privacy, surveillance, discrimination, as well as data collection, storage, and management. Exploring dilemmas tied to data science, artificial intelligence, robotics, etc. will allow students to consider their own data behaviors as well as trends and problems across contexts like organizations, social media, health, and education. Special attention in the class will be given to matters of policy and governing protocols around the world. Related challenges tied to Internet governance, misinformation, fake video, automation, etc., will also be explored

This course presents fundamental aspects of data science, including Python programming (e.g., data collection, cleaning, visualization), statistics, and mathematics (e.g., linear algebra and calculus). The course establishes the foundation for advanced data-intensive classes, providing both theoretical understanding and practical knowledge essential for comprehending Data Science and its applications.

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations.  The course includes laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.

This course provides an overview of the various concepts and skills required for effective data visualization. It presents the principles of graphic design, programming skills, and statistical knowledge required to build compelling visualizations that communicate effectively to target audiences. Visualization skills addressed in this course include choosing appropriate colors, shapes, variable mappings, and interactivity based on principles of color perception, pre-attentive processing, and accessibility.

Any non-core courses with the INFO prefix, including out-of-department courses, are considered electives. Out-of-department courses that are on the current pre-approved list may be taken without additional approval. However, if a student wishes to take a course that is not on the pre-approved list, it must first be approved by their faculty advisor.

Either an internship or a capstone project for a total of 3 units is required as part of the MSDS. The internship is intended to provide an opportunity for students to build on what they have mastered in the program and practice the knowledge and skills in the real world, whether corporate, institutional, nonprofit or otherwise. The internship should be relevant to student's degree competencies and contribute to the development and enforcement of the student's knowledge and skill sets in the fields of data science and information science. The capstone project is an opportunity for students to showcase what they have mastered in the program. It is based on a project plan that includes project goals, master's competencies addressed by the project, system design, implementation schedule, assessment plan and milestones. The project contributes to the development and enforcement of the student's knowledge and skill sets in the field of information science.