Blending together the fields of risk management and data analytics, the Master of Management, Risk and Analytics (MRA) equips students with cutting-edge analytics techniques to make smarter decisions and tackle complex problems with innovative solutions.
Innovative Coursework
Our curriculum equips students with the essential skills to navigate the complexities of today’s data-driven business landscape. You will learn to strategically manage risks, make data-informed decisions and utilize cutting-edge analytics tools to drive business success. Through a blend of rigorous coursework and hands-on learning, the MRA program prepares you to become a strategic leader capable of turning potential challenges into opportunities for innovation and growth.
Course Requirements
Students must complete 30 credit hours to satisfy the degree requirements – including 24 required credit hours and 6 hours of elective courses. Our innovative curriculum focuses on business analytics, risk management skills and practical experience – all in a hands-on learning environment.
This course takes students through the process of solution-driven data analysis, by applying finance, management, marketing, and operations applications. You will examine business cases and problems where data analysis is part of the decision-making process and become proficient in Excel methods commonly used in management. As a final project, students analyze a business problem from formulation to a solution, using data analysis.
As a continuation of the Data-Driven Managerial Decisions 1 course — students dive further into business cases and problems, becoming advanced in the data analysis decision-making process. You will learn how to perform linear relationship estimation among variables while becoming skilled in Excel methods commonly used in estimation. Students are also expected to complete a course project that follows a business problem from formulation to a solution using the methods covered in the course.
Structured framework for modeling and analyzing business decisions in the presence of uncertainty and complex interactions among decision parameters. The primary objective of this course is to improve modeling and analytical skills through a variety of realistic situations. The skills learned in this course are applicable in a wide variety of business decisions. Topics include decision models, value of information and control, risk attitude, spreadsheet application, and decision analysis cycle. Students will complete an interactive case study.
This course is designed around the full analytics lifecycle which encompasses the business problem, the data, the analysis, and the decision. Students will learn to identify and clearly explain business problems that can be addressed with analytics. They will learn to determine which analytic methods are best suited to solve particular problems and clearly explain the results of an analytic model. Emphasis will be placed on analyzing real data and understanding how analytical thinking can be applied to solve big data problems.
This course examines how to collect and process data to make it useful, how to validate, protect, and process data to make it available, and how to create a place to properly store data.
This course considers the use of analytics in decision-making in various contexts (e.g., business, public policy, personal). Students will discuss the importance of properly identifying causal relationships when using data to inform decisions and threats to reliable causal inference that commonly arise. Students will also discuss the potential consequences of making decisions based on flawed causal inferences.
An integrated approach to managing the risks that can prevent an organization from achieving its objectives, both financial and non-financial. Core elements of an effective enterprise risk management process. Links to management strategy. Risk assessment methodologies.
This course explores the use of data analysis techniques and tools that are useful for organizing and categorizing large volumes of information for use by executives to make strategic business decisions. The course exposes students to various Excel techniques to a business case to support management’s decision making and focuses on how data analysis can be used to inform management and the board about top strategic risk issues.
This course explores how forecasting and scenario planning are used to support management’s decision making. The course focuses on how data analysis techniques and tools are useful for organizing and categorizing large volumes of information for use by executives to make strategic business decisions and it exposes students to Monte Carlo Simulation and Optimization techniques (available in Excel and the Crystal Ball Excel add-on) to support strategic business decisions. The course then focuses on communicating this information to inform management and the board about top strategic risk issues.
This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. Students develop an understanding of the fundamentals of communication and alignment around concepts that are required for effective data presentation and it provides an overview and develop an introductory level of competency on the use of the Tableau software tool that can be used for data visualization.
This course leverages database management and data visualization tools to identify, analyze and summarize large volumes of data obtained from numerous data sources to effectively address a strategic business problem. Following the IMPACT data governance framework, students develop skills in articulation of a particular business challenge and then employ an analytical mindset to research, obtain, clean and manipulate publicly available data in order to evaluate potential risks impacting a strategic business decision. Students develop a final management report, including visualizations, to emphasize the importance of professional business writing to a higher-level audience.
Capstone Project (3 Credit Hours)
Students can take either of the following two practicum courses to fulfill this requirement.
This project-based course focuses on an applied approach to managing the risks that can prevent an organization from achieving its objectives, both financial and non-financial. Students work in teams to address real problems in real organizations.
This course focuses on solving a real-world business problem with a heavy data analytic component. The business problem will vary according to the client but could include problems from marketing, finance, supply chain, or other concentration areas.
Electives (6 Credit Hours)
Electives will include a variety of courses integrating the concepts of risk and data analytics, including regulation and compliance, liquidity, business continuity and technology and innovation. Additional electives include the Business Sustainability practicum, courses in artificial intelligence and machine learning, and study abroad courses.
Additional Requirements
To provide support with the technical skills the program requires, all students complete a self-paced, virtual boot camp before the start of the program. The boot camp was designed internally by our faculty, so it aligns directly with the main curriculum and ensures our students are set up for success on Day 1.
“[Since] we gained some hands-on experiences with tools like ChatGPT and RStudio, I’m actually starting to use these more in my day-to-day responsibilities at work.”
