From Seattle University's 2020-2021 Undergraduate Catalog.
All undergraduate courses are 5 credits, unless otherwise noted.
Syllabi information is for reference only; information may not be current.
Introduces students to the legal and ethical dimensions of data analytics, data mining, predictive analytics and related techniques, collectively “big data”. Rapidly evolving technologies and the increasingly complex collection, aggregation, analysis and use of data creates opportunities for financial gain and social good, but also for dangers in areas such as privacy, discrimination, and violations of basic human dignity. This course will be framed as a contextual examination of what law exists to regulate the risks of big data at various stages of the data life-cycle, and to consider ethical responses that may be employed to address the related concerns where no law exists. This is a dynamic course which presents a rich basis for student contemplation and discussion of the central questions for “big data,” including the acquisition and use of data, professional and social responsibility in the application of modern information technologies; the efficacy of management by algorithm and the loss of human control in using artificial intelligence.
Prerequisite: UCOR 2910
Introduces the modern concepts of application programming for business analytics, including data types, expressions, control structures, functional abstraction, object-oriented programming, data management, SQL, and application programming interfaces (APIs).
Prerequisite: ECON 3100
Introduces the data visualization and communication while teaching and applying statistical software, like R. Visualization will be taught for a variety of data types and visualizations using short business communications.
Prerequisite: BUAN 4210
Introduces the concepts and practices of data mining and analyzing a large amount of data, or big data, in business using data mining. It provides a review of supervised and unsupervised learning methods along with statistical theories for learning methods. Contemporary methods for data mining in statistical packages like R, and of working with big data, such as Hadoop and MapReduce, will also be taught.
Prerequisite: BUAN 4220