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.

Gives students an understanding of how contemporary technology impacts personal security, privacy, and liberty. Studies threat modeling, proper account practices, data collection methods, and vulnerabilities in digital and physical environments. Application of security measures and privacy enhancing technologies to address various use cases.

Introduces the fundamental concepts of database analysis, design, and implementation with emphasis on the relational model. Topics include SQL, data modeling, data normalization, tables, queries, data mining, security, database administration, and backup/recovery.

An introduction to the various technical and administrative aspects of information security and assurance. Provides the foundation for understanding the key issues associated with protecting information assets, determining the levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features.

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.

Studies undertaken by academically qualified students under the guidance of a faculty member, with the approval of the chair of the Department of Entrepreneurship, Technology, and Law. May be repeated under different topics for a maximum of six hours credit.

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.