From Seattle University's 2023-2024 Graduate Catalog.
All graduate courses are 3 credits, unless otherwise noted.
Introduces computer programming for data extraction, cleaning, transformation, integration, data mining, statistical analysis, data visualization, and others. Class projects will be drawn from real world examples. Designed for students who have prior experience with computer programming.
Prerequisites: All requirements for first registration in the MSBA program.
Further develops (beyond BUAN 5201) skills and techniques in computer programming for data extraction, cleaning, transformation, integration, data mining, statistical analysis, data visualization, and others. Class projects will be drawn from real world examples. Designed for students who have taken BUAN 5201.
Registration Restriction: MSBA students only
This course teaches the essential and practical skills necessary to communicate information about data clearly and effectively through written, oral and graphical means. Students will learn and practice with advanced visualization tools to effectively communicate. The course will build from the understanding of data to the presentation of the analysis. Data visualization “storytelling” will provide tools to effectively: communicate ideas, summarize, influence, explain, persuade and provide evidence to an audience. Visualization can convey patterns, meaning, and results extracted from: multivariate, geospatial, textual, temporal, hierarchical, and network data. During the course students will deliver presentations using these techniques and they will also learn to critically evaluate other presentations.
Prerequisites: All requirements for first registration in the MSBA program.
This course helps you address traditional problems in management and marketing with data and data analysis .in ways that could not even be imagined a few years ago. You will think critically about how effective stakeholder management can enable the realization of business value, and understand how poor handling of data and relationships can negatively impact organizational outcomes. This course is required but may be waived with a business undergraduate degree or significant business coursework.
Registration Restriction: MSBA students only
This course introduces several important modeling approaches for decision-making problems. The first part of the course focuses on deterministic optimization problems including linear, integer, and possibly dynamic programming. Applications that may be used during the course include: allocation of advertising and sales effort, artificial intelligence models, revenue management, production and distribution systems planning. The second part of the course addresses decision-making under uncertainty. Topics include: a brief review of probability theory, an introduction to stochastic processes, and Monte Carlo simulation. Illustrations used during this part of the course may be drawn from areas such as consumer behavior (learning, purchase timing, purchase incidence, or brand choice), scheduling of operations, performance of computing systems, and sales forecasting. The emphasis throughout is on understanding the problem, formulating a suitable model, finding a solution, interpreting it, and performing sensitivity analysis. The course seeks to provide an intuition for how different techniques work, along with experience in applying them to real problems, and in presenting results and recommendations in a clear and persuasive manner to specialists and non-specialists alike. Students will complete a short collaborative term project that requires them to recognize, model, and solve a real-world problem using the methods learned in the course.
Prerequisites: All requirements for first registration in the MSBA program.
Because of data, managers can measure, and hence know, radically more about their business, and directly translate that knowledge into improved decision-making and performance. The knowledge created by the data analysis can also help firms to make better financial decisions as well as predicting the reaction by the financial markets or more specifically providers of capital. For example, should we restructure our business? You would want to know: How will the investors react to the announcement of the restructuring? Will it cause a sell-off in the market, therefore, create a capital crunch? Will this decision have implications for the future performance of the company? This course will provide a basic methodology to help you answer these kinds of questions with an emphasis of performance and financial markets. We will specifically cover firm valuation with an emphasis on accounting information, dividend policy choices, and compensation policies of real-time firms that are publicly traded. The strength of the course stems from the idea that we will be using real-life business decisions, with real-data and will use real modeling experiences that have been widely used in the industry.
Registration Restriction: MSBA students only
This course will examine the opportunities and challenges introduced by business analytics through the perspectives of the law and ethics. Rapidly evolving technologies that permit the collection, storage, aggregation, analysis, and use of data create opportunities for financial benefit and the common good, but also create challenges to legal rights such as privacy, equality, and dignity, and to ethical values, such as autonomy, trust, and virtue. The course will be framed as a contextual examination of business analytics to facilitate learning about legal and ethical standards for private organizations using data analytics techniques in various stages of the data life-cycle. This is a dynamic course which presents a rich basis for student learning and contemplation of central questions for “big data”, including issues related to acquisition and use of data; professional and social responsibility in the application of modern technologies; the efficacy of management by algorithm; and the loss of human control in using artificial intelligence. The following are examples of legal and ethical issues that may be included, subject to time constraints: in law: information privacy law such as U.S. tort law, federal statutory and administrative law, and constitutional protection of civil liberties; European Union data privacy regulation; cyber-intelligence and cyber-security regulation; contractual liability, specifically with respect to third party reliance on data analysis; the law of negligence; and agency law; in ethics: adverse effects of data collection on vulnerable populations; transparency and honesty in the cleaning, processing, and visualization of data; introduction of the machine equivalent of implicit bias in feature selection; and responsibilities when using data analysis as a tool to guide human decision making. Registration restrictions may be bypassed by the department with permission of instructor.
Registration Restriction: MSBA students only
Prerequisites: Business Calculus, Business Statistics, Computer Programming
This course introduces data management for business analytics using SQL. Data mining for business analytics requires pre-processing of data before applying machine learning algorithms. We discuss this pre-processing of data, including data cleaning, integration, discretization, normalization, reduction, and others as well as basics of SQL. No prior knowledge in SQL is required.
Many problems in today’s business require traditional and nontraditional forms of data analysis. In particular, rapid developments in data collection and storage technologies have led to big data sets and new questions; - Amazon collects purchase histories and item ratings from millions of its users. How can it use these to predict which items users are likely to purchase and like? - Yahoo news acts as a clearinghouse for news stories and collects user click-through data on those stories. How should it organize the stories based on the click-through data and the text of each story? - How does Netflix recommend movies to each of its users? An expert’s answer to any one of these questions may very well contain enough material to fill its own course, but basic answers stem from the principles of statistical and machine learning. This course covers fundamental machine learning methodologies on supervised learning and unsupervised learning.
Prerequisite: ECON 5100
Big Data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process within a tolerable elapsed time. Big data tools have been evolved to the application of analytic techniques to very large, diverse data sets that often include varied data types and streaming data, i.e., parallel computing, MapReduce, NoSQL, etc. This class will discuss big data tools, analysis and use cases.
The Capstone is an application of data analytics in the planning and execution of real-life development project for an industry partner. Students work individually or in small teams to define and carry out an analytics project from beginning to end. Key steps include: scoping the project, locating an industry partner, formalizing a question, finding data sources, determining the method of analysis, implementing the analytical procedure, and communicating the results to the client. This process will help students integrate what they have learned in multiple courses, and apply their expertise to solve a problem for a real-world enterprise.
Prerequisites: ECON 5300; IS 5201, 5305, 5315; BUAN 5210, 5260, 5310.
Special topics courses are designed: (1) to introduce courses on new areas of research, emerging issues, and new tools in business analytics;(2) to allow visiting faculty to teach subjects that are their main focus of interest; and (3) in general to allow discussion of specialized content that is not part of the standard curriculum. The number of credits will vary between 1 and 3, depending on the contact hours required.
Prerequisites: Permission of program director and instructor.
For more about internships, visit the Albers Career Center. MSBA Students only.
Individualized reading, research, or development of models and solutions for practical or theoretical problems on a specific topic of interest to the student, and approved by an instructor. May be taken in lieu of BUAN 5510, Capstone Project in Business Analytics, with permission of the program director. Grading option negotiated with instructor for CR/F or letter grade (student option), except if taken in place of BUAN 5510, in which case the course will have a letter grade only. The program of study and conference times must total 30 hours of study and contact hours for every one-credit taken. (1 to 3 credits, Pass/Fail)