Information visualization is the graphical representation of data to aid understanding, and is the key to analyzing massive amounts of data for fields such as science, engineering, medicine, and the humanities. This is an introductory undergraduate course on Information Visualization based on a modern and cohesive view of the area. Topics include techniques such as visual design principles, layout algorithms, and interactions as well as their applications of representing various types of data such as networks and documents. Overviews and examples from state-of-the-art research will be provided. The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques. Fulfillment: CS Electives, Data Science Data Analysis Required; Data Science Courses for Concentration in Artificial Intelligence. Prerequisite or Co-requisite: Data Structures. Students must be CS or DS major and have junior or senior standing.
This course has three goals. First, the mastering of a modern object-oriented programming language, enough to allow students to tackle real-world problems of important significance. Second, gaining an appreciation of computational thinking, a process that provides the foundations for solving real-world problems. Finally, providing an overview of the very diverse and exciting field of computer science – a field which, arguably more than any other, impacts how we work, live, and play today. Prerequisite: Introduction to Computer Programming or placement exam. Equivalency: This course counts for CSCI-UA 101. Fulfillment: Core Curriculum Requirement Algorithmic Thinking; Computer Science Major Required Courses; Computer Systems Engineering Major Required Courses; Data Science Major Foundational Courses; Electrical and Systems Engineering Major Required Major Courses.