Schedule of Classes

Develop spreadsheet applications for analyzing and solving problems. Learn how to gather, store, organize, secure and disseminate data with spreadsheets and databases. Learn how to convert data into information that is beneficial to supporting business decisions.

Students will learn commonly used data analysis tools and techniques. They will learn how to use and apply software that allows business professionals to gather, store, access, and analyze data to aid in decision making. The course will teach students how to discover and communicate information from data through the use of basic, intermediate, and advanced functions and tools in commonly used spreadsheet and database software. Each student will learn about the visual representation of data, optimization techniques, queries, pivot tables, reporting tools, data storage, and more.

Topics of special interest in management information systems, which may vary each time the course is offered. Topic and prerequisite stated in current Schedule of Classes. May be repeated under different topics for a maximum of six hours credit.

Gives students an understanding of basic network design concepts and an opportunity to apply them in a business context. Studies the functionality, performance and management of multiple network designs. Application of the theories, design and technologies utilized in modern business data communications networks.

Information systems in business applications. Emphasis on relationship of information systems planning to overall business goals, policies, plans, management style, and industry condition; analysis, design, and implementation of information systems. Overview of future trends in data management.

Explores data analysis and statistical methods as well as best practices for continuous iterative investigation of past business performance to gain insights and drive business planning. Exposes the students to several aspects of Business Analytics. Investigates data analytics fundamentals, data cleansing and transformation, and supervised/unsupervised data mining techniques for tasks like targeted mailing campaigns, customer segmentation, customer churn, fraud detection and market basket analysis.

Visual illustration of how to better understand data, present clear evidence of findings to an intended audience, and tell appealing stories through data graphics. The topics covered include but are not limited to: design principles, multivariate displays, geospatial displays, dashboards, interactive and animated displays. Some knowledge of basic programming (in any language) will be helpful, but not required. We will use several tools to refine our data and create, edit, alter, and display their visualizations.

Provides students with hands-on experience with all phases of a security assessment for a live client. Students are responsible for planning the assessment, executing assigned tasks, and reporting results.

Applies the concepts and skills learned by Business Analytics undergraduate students. Students are required to work on a team with a business client on an analytics project.

Explores data analysis and statistical methods as well as best practices for continuous iterative investigation of past business performance to gain insights and drive business planning. Exposes the students to several aspects of business analytics. Investigates data analytics fundamentals, data cleansing and transformation, and supervised/unsupervised data mining techniques for tasks like targeted mailing campaigns, customer segmentation, customer churn, fraud detection and market basket analysis.

Visual illustration of how to better understand data, present clear evidence of findings to an intended audience, and tell appealing stories through data graphics. The topics covered include but are not limited to: design principles, multivariate displays, geospatial displays, dashboards, interactive and animated displays. Some knowledge of basic programming (in any language) will be helpful, but not required. We will use several tools to refine our data and create, edit, alter, and display their visualizations. Cross-listed with MIS 473. The graduate level course will have additional requirements beyond those of the undergraduate course.

Applies the concepts and skills learned by data science and analytics graduate students. Students are required to work on a team with a business client on an analytics project.