12 courses with the subject BUA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BUA 226Introduction to Big Data1
Fall This course provides an overview of the non-traditional strategies and technologies needed to collect, organize, process, and gather insights from large data. It will introduce modern concepts, terminologies, and technological frameworks regarding big data in the industry. Topics include big data defining characteristics and technologies, the role of a data scientist, data warehousing, and emerging trends in big data. 1 credit hour
Fall This course introduces the fundamentals of data visualization and practices communicating with data. It includes hand-on experience with leading industry data visualization tools. Topics include introductory concepts in data visualization, interaction with data, design principles, effective storytelling with data, and advanced data charts design. 1 credit hour
Fall This course introduces one of the most popular and powerful programing languages in both industry and academia. R programming is the fundamental skills for business analytics and data science. This one credit course includes hand-on experience using R in basic data analysis. Topics include the fundamentals of R syntax, conditional statements, functions, classes, debugging, and reading and writing data in R for basic descriptive analysis. 1 credit hour
This course provides an overview of current topics in Big Data with corresponding technologies to organize and present Big Data. Excel and other third-party software will be introduced. The objective of this course is to train students to use appropriate tools to organize data and visualize information to explore managerial insights from Big Data; and then be able to create an effective storytelling presentation to deliver findings for data driven decision making. 3 credit hours
Spring This course introduces fundamental machine learning methods used in business analytics and data-driven decision making. Students learn key techniques such as classification, neural networks, regression, clustering, and model evaluation, with an emphasis on practical implementation using Python. Through applied exercises and real-world case examples, students gain the ability to build, interpret, and apply predictive models to support analytical and organizational insights. 3 credit hours
Fall/Spring/Summer This course requires planned and supervised work experience at selected cooperating organizations, for application of classroom learning. Students will gain meaningful, pre-professional work experience in their field of study. Students are required to spend a minimum of 120 hours work on site. Internships require students to meet periodically with a faculty supervisor, provide a written deliverable, and participate in an end-of internship evaluation. Grading will be Pass/Fail.
