Subject: Business Analytics (BUAN)

CRN: 21962

Online: Some Synchronous | Lecture

Online

Requirements Met:
     MS in Business Analytics
     Part-time MBA

  Jordan Barlow

This course teaches students how to perform data analysis using spreadsheet-based models and interactive data dashboarding tools, including PowerBI, to effectively and efficiently solve business problems. Students will learn how to both build and communicate about these models to drive managerial decision making. As part of this process, students will apply basic data cleansing and modeling, emerging technologies such as AI, and ethical concerns of working with data.

3 Credits

Subject: Business Analytics (BUAN)

CRN: 21963

Blended Online & In-Person | Lecture

Minneapolis: Schulze Hall 421

Minneapolis: In Person

Online

Requirements Met:
     MS in Business Analytics
     Part-time MBA

  Dave Wehling

This course will focus on developing the ability to understand the business needs for data insights, crafting those into an analytics problem statement, and developing a coherent and persuasive narrative of any data findings. Students will learn to create well-crafted data narratives and dashboards for business leaders while being able to translate insights into managerial decisions. Students will also be able to prepare raw data sets for their data narratives, executive summaries and technical memos. The course focuses on mastering these fundamental data narrative and storytelling abilities while leveraging AI, Tableau, and other data visualization tools to assist in the process. 

3 Credits

Subject: Business Analytics (BUAN)

CRN: 22243

Blended Online & In-Person | Lecture

Online

Online

Requirements Met:
     MS in Business Analytics

  Mark Price

Harnessing AI for Competitive Advantage” is a graduate course designed for business leaders to strategically implement artificial intelligence for growth and competitive edge. This course integrates AI technology with business strategy, facilitating the effective use of AI through real-world case studies and projects. Students will delve into AI’s role in enhancing customer experiences, improving operations, and driving innovation. Key areas include generative AI, machine learning, natural language processing, and computer vision. Participants will also develop strategies for AI adoption, assess the impact of AI initiatives, and navigate ethical considerations to ensure responsible AI deployment. Ultimately, students will acquire skills to lead AI-driven transformations and foster sustainable growth in an AI-powered business landscape.

3 Credits

Subject: Business Analytics (BUAN)

CRN: 21965

Blended Online & In-Person | Lecture

Minneapolis: Terrence Murphy Hall 253

Online

Requirements Met:
     MS in Business Analytics

  Yulia Vorotyntseva

This course provides students with a basic understanding of statistics – the science of gathering, analyzing, interpreting and presenting the data. Statistics is one of the two pillars powering machine learning and artificial intelligence, with the second pillar being computing software. Students will learn methods for summarizing data, both numerically and graphically, and for drawing conclusions from sample data. Statistical analyses will be carried out using Python and statistical software, further introducing the students to applications of AI. The focus of the course is on how statistical methods can be applied to business problems to improve outcomes; emphasis is placed on the collection and leveraging of data, and the interpretation and presentation of results.

3 Credits

Subject: Business Analytics (BUAN)

CRN: 21966

Blended Online & In-Person | Lecture

Minneapolis: Terrence Murphy Hall 254

Minneapolis: In Person

Requirements Met:
     MS in Business Analytics
     Part-time MBA

  James Beal

This course will focus on students ability to identify and build models that will provide insights into the decision making process. This course will utilize the analytics problem solving process from problem identification, methodology selection, model building and analysis, to model implementation.  In this course students will learn a variety of predictive modelling techniques including advanced statistical models and machine learning based models.  This course will utilize a variety of industry applications to learn how to apply their predictive modelling skills. Prerequisite: OPMT 600 or SEIS 631 or BUAN 640

3 Credits

Subject: Business Analytics (BUAN)

CRN: 21967

Blended Online & In-Person | Lecture

Minneapolis: Schulze Hall 301

Online

Requirements Met:
     MS in Business Analytics

  Scott Martens, Mark Price

This application-focused course provides the opportunity for students to experience a real-time business analytics project. Under faculty guidance and mentoring, small teams of students will work together to implement the breadth of methods and skills developed throughout the MSBA program to manage all aspects of client and project management; develop the project deliverables including business problem analysis, data transformation and analysis; and presentation of the results at the client site. The course will begin with limited on-campus meetings, then transition to a flexible “directed study” format with regular required check-ins with the faculty leader, providing ample time for the team to complete the project work. Teams will use online collaboration software tools for communication and project coordination. Prerequisite: 21 credits completed. 

3 Credits

Subject: Ops & Supply Chain Mgmt (OPMT)

CRN: 22172

Blended Online & In-Person | Lecture

Minneapolis: Schulze Hall 407

Online

Requirements Met:
     MS in Business Analytics
     Part-time MBA
     LLM/MSL Elective

  Muer Yang

This course provides students with a basic understanding of the role of statistics in the gathering of data, the creation of information and its use in decision-making. Students will learn methods for summarizing data, both numerically and graphically, and for drawing conclusions from sample data. Statistical analyses will be carried out using the computer and statistical software. The focus of the course is on how statistical methods can be placed on the design of statistical studies, collection of data, and the interpretation of results (rather than the details of computation). Prerequisites: NONE.

3 Credits