Data Sciences Minor

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures. Prerequisite: CIS 268 or EGR 121. (3 units; Spring)

This course is a comprehensive introduction to data management in organizations. It establishes the data management foundation for the computing major. Topics include conceptual and logical data modeling, entity relationship and relational data modeling, and database design and implementation using the SQL programming language. (3 units; Online)

The course provides an overview of the theoretical foundation and applied use of Geographic Information Systems (GIS). At the conclusion of the course, students will have working knowledge of GIS and their appropriate application in various disciplines and organizational settings. The historical development of innovative and collaborative geographic information tools that assist with managing the challenges associated with positional data will also be introduced. Students will demonstrate acquired knowledge via application of the fundamental and principal concepts of geographical information systems by completing a series of lab assignments and a culminating research project. Prerequisites: EGR 121 and CIS 265. (3 units; Fall)

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered. Prerequisite: STA 144. (3 units; Spring, even years)

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project. Prerequisites: EGR 121 or CIS 268, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional, approved statistic course. (3 units; Fall)

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools. Prerequisites: EGR 121 or CIS 268, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional, approved statistics course. (3 units; Fall)

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted. Prerequisites: EGR 121 or CIS 268, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional, approved statistics course. (3 units; Spring)