From Seattle University's 2023-2024 Graduate Catalog.
All graduate courses are 3 credits, unless otherwise noted.
This course introduces the basic concepts of programming for data analytics, including data types, expressions, control structures, functional abstractions, object-oriented programming, application programming interfaces (APls), and the libraries for data analysis, manipulation, multi-dimensional arrays, visualization, and others.
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.
This course introduces data mining or knowledge discovery using machine learning algorithms 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, market basket analysis/association rules, artificial neural networks, and others. Students are expected to analyze real-world data in business using machine learning software.
Prerequisite: IS 5200
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.
This course introduces how to perform web analytics for business. Web analytics includes measuring and analyzing web traffic data such as contents of the web sites, social activities, and advertising performance. It can be used as a tool for conducting market research, and assessing/ improving the effectiveness of a web site. Topics include the Internet, web,mobile apps, content management systems, cloud computing, and others.
6 CREDITS
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
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 5940)
For more about internships, visit the Albers Career Center
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)