from Seattle University's 2017-2018 Graduate Catalog
All graduate courses are 3 credits, unless otherwise noted.
Syllabi information is for reference only; information may not be current.
New course for the 2017-2018 academic year.
This course introduces the modern concepts of application architecture and programming for business, including data types, expressions, control structures, functional abstraction, object-oriented programming, data management, application programming interfaces (APls), service-oriented architecture, and Microservices architecture. No prior programming experience assumed.
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.
Concepts, tools, and strategies for understanding and exploiting opportunities associated with electronic commerce; focus on the strategic aspects of marketing using the Internet. The Internet is dramatically altering the way business is conducted on a local and global basis, changing the way organizations conduct business, provide customer service, interact with internal and external stakeholders, advertise, develop products, build brands, generate new prospects, monitor the marketplace, and distribute products and services.
This course introduces the management and analysis of corporate data. Topics include conceptual data modeling, relational database systems, data warehousing, and data administration, as well as SQL. Students are expected to understand the managerial challenges and solutions of corporate data management.
- Syllabus: IS 5305-01 Ben Kim - Fall 2017
- Syllabus: IS 5305-02 Ben Kim - Fall 2017
- Syllabus: IS 5305-03 Jari Williams - Fall 2017
- Syllabus: IS 5305-04 Jari Williams - Fall 2017
This course introduces data mining or knowledge discovery to provide business intelligence by analyzing massive amounts of data to find interesting patterns that can be used to assist decision making or provide predictions. Topics include decision trees, Bayesian classification, clustering, sequence clustering, association rules, time series analysis, neural networks, and others. Students are expected to analyze real-world data in business using data mining software.
- Syllabus: IS 5310-01 Misuk Lee - Spring 2017
- Syllabus: IS 5310-01 Ben Kim - Winter 2017
- Syllabus: IS 5310-02 Ben Kim - Winter 2017
Big data analytics is the application of analytic techniques to very large, diverse data sets that often include varied data types and streaming data. Big data analytics explores business and customer interactions from data that seldom finds its way into a data warehouse or standard report This data is often unstructured data coming from sensors, devices, third parties, Web applications, and social media - much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior. New methods of working with big data, such as Hadoop and MapReduce, also offer alternatives to traditional data warehousing. This class will define big data and big data analytics techniques and review big data use cases.
The Internet is becoming our new habitat for daily life and business. This class introduces the fundamentals of technologies on the Internet, including communication protocols and design of Internet applications. Discuss business strategies in this new environment in various market segments.
The Capstone is an application of data analytics in the planning and execution of a one-quarter-long real-life development project. Students work in teams to define and carry out an analytics project from initial requirements analysis to final implementation. Primary tasks include an identification of datasets, ETL (Extraction, Transformation, and Loading), building data mining models, and validation. This activity will culminate in a formal presentation of results at the end of the quarter.
Prerequisites: IS 5305 and IS 5310; or equivalents
IS 5910 Special Topics Courses
See administrative office for prerequisites and course descriptions.
This course focuses on the management of technology in a given region of the world, and involves visiting a country in question to gain a better understanding of the issues facing managers in that environment. Location of tour can vary. Check with the department for details. (formerly IS 594)
IS 5950 Internship
For more about internships, visit the Placement Center
IS 5960 Independent Study
Independent study. Individualized reading and reporting on a specific topic approved by an instructor. The program of study and conference times must total 30 hours of study and contact hours for every one-credit taken. Grading option negotiated with instructor for CR/F or letter grade (student option). (1 - 3 credits)