University of North Dakota (UND)
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Quantitative Methods and Decision Theory MGSC 2103-0

MGSC-2103-0, MGSC 5151-12
Open Closing on December 6, 2024 / 2 spots left
Cape Breton University
Cape Breton University
Sydney, Nova Scotia, Canada
Zainab Almukhtar
Assistant Professor
5
Experience
1/3 project matches
Dates set by teams
Preferred companies
Anywhere
Any company type
Any industries
Categories
Data analysis Data modelling Operations
Skills
quantitative research constructive feedback strategic decision making mathematical modeling problem solving decision making probability linear programming microsoft excel health administration
Learner goals and capabilities

The course of Quantitative Methods equips students with essential skills in applying quantitative techniques to solve real-world problems in business, healthcare, management, and other industries. Learners will focus on interpreting data, formulating mathematical models, and making informed decisions based on their analysis. With foundational knowledge in probability, regression, linear programming, and decision-making under uncertainty, students will use tools like MS Excel and Excel QM to drive data-backed insights.


Employers are expected to provide regular guidance, offer access to all relevant project information, participate in virtual feedback sessions. Your constructive feedback will help students refine their problem-solving and decision-making skills through practical application.

Learners
Post-graduate
Beginner levels
7 learners
Project
39 hours per learner
Educators assign learners to projects
Teams of 2
Expected outcomes and deliverables

At the end of this collaboration, employers will receive:

  • A final report detailing the problem analysis, model formulation, and key recommendations based on quantitative methods.
  • A final presentation where student groups will present their findings and answer any questions about the project.


Project Examples

Ideal projects will involve the application of quantitative methods to real-world scenarios, allowing students to develop models, interpret data, and make decisions. Examples include:

  1. Optimization of Resource Allocation: Create a linear programming model to optimize the allocation of resources in a business setting.
  2. Risk Analysis in Healthcare: Apply probability and decision models to assess risk and improve decision-making in a healthcare project.
  3. Exploring the utility of a customer or a provider: Using utility functions, investigate the preferences of customers and how that directs decision making.
  4. Sales Forecasting for a Retailer: Use regression analysis and forecasting techniques to predict future sales trends for a retail company.
  5. Inventory Management Solutions: Develop a model to optimize inventory management decisions, reducing excess stock and minimizing costs.
  6. Decision Making: Application of Linear Programming Approaches to Decision making.


Companies must answer the following questions to submit a match request to this experience:

Does your project require the application of quantitative decision-making models or data analysis?

Will you be able to provide the necessary data or context for students to work on the project?

Does your project require the use of Excel or similar tools for model development and analysis?