M.S. in Modeling, Simulation and Data Analytics

Course Descriptions

Each subject within the Modeling, Simulation and Data Analytics program includes a course teaching skills followed by a course focused on skill practice. The descriptions of each subject area are shown below:

Systems Modeling & Simulation: Skills & Practice

Often, complex dynamic systems cannot be easily modeled using standard quantitative mathematical tools. In these cases, representing the system visually as a series of interconnected elements, then simulating the dynamic behavior of the system numerically using modern simulation software is a more productive and effective way to develop insights into the behavior of the complex system under investigation. The mathematical tools and techniques necessary to perform these tasks will be explored in this two-course sequence using the Stella software package to assist in modeling, simulating, analyzing and interpreting the behavior of complex dynamic systems.

Analytical Modeling & Applied Mathematics: Skills & Practice

Typically, mathematical expressions must be developed to translate complex, real-world problems into simplified mathematical expressions (models) that can be solved, analytically or numerically, in order to develop insights into the behavior of the system under investigation. The mathematical tools and techniques necessary to perform these tasks will be explored in this two-course sequence using the Mathematica software tool to assist in performing quantitative mathematical analysis and in graphically interpreting the resulting analytical solutions.

Data Analysis & Applied Statistics: Skills & Practice 

A statistical analysis of modeling and simulation results is normally needed in order to translate these results into meaningful insights about the complex systems under investigation. The applied statistics tools and techniques needed to develop these insights will be explored in this two-course sequence primarily using the “R” software environment for statistical analysis and graphics as implemented within Mathematica. Other statistical analysis tools generally available to working professionals will also be discussed (e.g., Excel, SAS, SPSS, etc.).

Data Visualization & Decision Support: Skills & Practice

Insights developed during the modeling, simulation and data analysis process must ultimately be visualized and communicated in a compelling way in order to recommend specific paths of action and support decision-making and strategic planning functions within an organization. Data visualization techniques will be explored in this two-course sequence using Tableau advanced visualization software, along with the native graphics and communications capabilities available in the other software packages used in this program (i.e., Stella, Mathematics, R) and those generally available to working professionals (e.g., PowerPoint, Excel, Prezi, etc.).

Capstone Project: Proposal & Completion

Students will be challenged to synthesize the modeling, simulation, analysis and visualization tools and techniques learned in the curriculum in a culminating original capstone project. In this two-course sequence, students will be expected to identify an original problem, and perform appropriate research in order to identify data sets and prepare/defend a viable project proposal. Students will then be expected to demonstrate mastery of the software tools and techniques explored in this curriculum by modeling a complex system associated with the project topic, simulating appropriate results, verifying the simulation results versus available data, developing insights using appropriate data analytics, and communicating these insights in a compelling and effective way.

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