This course introduces students to the fundamentals of web and mobile design and development. Students will learn the core building blocks of the web, including HTML, CSS, and JavaScript, and apply visual design principles to create responsive and accessible interfaces. Topics include preparing digital images and graphics for the web, layout and typography, color and animation, and user experience concepts. Students will also be introduced to traditional and generative AI workflows and tools used in professional practice for coding, prototyping, and project development. Emphasis is placed on hands-on exercises and projects to practice both design and implementation, preparing students for more advanced coursework in web and mobile design.

This course surveys the fundamental concepts and theories used in organizing and structuring data that arise in a wide range of contexts (e.g. lab sciences, economic, social services, computing system performance, environmental sustainability). Course topics include data organization theory, basic relational model theory, normalization theory, relational algebra, data quality, data integrity and security, and mapping a data model into a database schema. Students will learn how to construct and use databases while maintaining the security and integrity of data as well as develop AI literacy in the context of data model design, learning how to apply and critique Artificial Intelligence responsibly and ethically. Hands-on assessments are included in this course.

This course builds on the basics of web page development that are presented in Web & Mobile I and extends that knowledge to focus on theories, issues, technologies, exploration of AI tools, as they apply to the design and development of web sites. An overview of web design concepts, including usability, accessibility, information architecture, and design process in the context of the web will be covered. Introduction to web site technologies, including HTTP, web client and server programming, and dynamic page generation from a database also will be explored.

Rapidly expanding volumes of data from all areas of society are becoming available in digital form. High value information and knowledge is embedded in many of these data volumes. Unlocking this information can provide many benefits, and may also raise ethical questions in certain circumstances. This course provides students with a hands-on introduction to how interactive data exploration and data mining software can be used for data-driven knowledge discovery, including domains such as business, environmental management, healthcare, finance, and transportation. Data mining techniques and their application to large data sets will be discussed in detail, including classification, clustering, association rule mining, and anomaly detection. In addition, students will learn the importance of applying data visualization practices to facilitate exploratory data analysis.

Students perform paid, professional work related to their program of study. Students work full-time during the term they are registered for co-op. Students must complete a student co-op work report for each term they are registered; students also are evaluated each term by their employer. A satisfactory grade is given for co-op when both a completed student co-op report and a corresponding employer report that indicates satisfactory student performance are received.

An introductory course in software engineering, emphasizing the organizational aspects of software development and software design and implementation by individuals and small teams within a process/product framework. Topics include the software lifecycle, software design, user interface issues, specification and implementation of components, assessing design quality, design reviews and code inspections, software testing, basic support tools, technical communications and system documentation, team-based development. A term-long, team-based project done in a studio format is used to reinforce concepts presented in class.