Business Analytics

Introduces students to programming languages Python and R. Students learn within the coding environment core programming concepts such as data structures, conditions, loops, variables, and functions.

Provides students with a base level understanding of analytics in business. It will include a discussion of key topics, such as big data, analytics (including predictive/prescriptive), machine learning, Internet of Things, data mining and data science. This course will begin by examining these key topics, then move into an in-depth focus on the analytics process. Using case studies and application to real-world scenarios, students will experience how to apply the data analytics process to business situations. Next, the course will focus on data communication and visualization principles. Finally, students will be introduced to common data analytics software applications and use one, or more, of these applications to complete and end-of-term project.

Introduces the importance of communicating the results of analysis as a critical component to the successful adoption of analytics in an organization. This course focuses on the principles of data communication and creating a mindset that thinks beyond just the data. The ability to communicate the meaning within the data and drive action requires effective communication skills and strategies.

Explores the techniques of problem definition, determination of system requirements, and design of computer applications. Topics include development life cycle, cost determination, data requirements, and systems documentations.

Emphasizes the importance of multiple perspectives and keeping an open mind in the field of analytics. This course will examine how analytics is applied in the world today. Using case studies from business, media, sports, politics, and others, students will learn how analytics and the analytics process is applied more broadly than jut in the boardroom. Students will be challenged to apply analytics to a variety of situations and illustrate the way in which analytics can help solve problems across disciplines, while also articulating the risks and challenges also associated.

Discusses database concepts and design, data models, query language facilities, and data protection considerations and methodologies. Topics include relational and no-SQL database models.

Develops student skills in large data set acquisition, cleansing, manipulation and visualization utilizing various tools such as Google Analytics, Alteryx, PowerBI, and others.  

Explores the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. Topics include: association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

Allows students the opportunity to apply what they have learned within their chosen discipline. Through partnerships with local organizations and companies, students will get hands-on experience working with an organization to solve a problem using analytics. Students will work in groups to collaborate on these projects, which are completed under the guidance of an instructor in their discipline.