Concordia's advanced degree in Management Science & Quantitative Methods (MSQM) provides professionals across industries and career levels with the skillsets needed to make strategic, data-driven decisions within their organizations. Throughout the program, students will explore analytical methods and tools to understand, interpret and apply data to complex, real-world problems, while honing leadership skills critical in today’s global business environment.
MSQM is ideal for busy, working professionals with a course schedule accommodating a work, life, and academic balance. An accelerated online format offering students a short break between courses. During fall semester, students select course options that align with their experience level and career goals. The program culminates with a professional capstone project focused on relevant business issues and challenges.
The requirements for a Master of Science in Management Science and Quantitative Methods are listed below.
|Data Analysis & Visualization
|Advanced Operations Management/Research
|Integrative Capstone Experience I
|Integrative Capstone Experience II
|Select a minimum of 20 credits from the following:
|Supply Chain & Risk Management
|Leadership: Theory and Application
|Statistics & Research Analysis
This course focuses on the development and analysis of cost information used by management decision makers to evaluate and improve company performance. It includes product cost analysis, profitability planning, performance analysis and emerging cost strategies.
The main purpose of the course will be to learn about supply chain decisions. The students will be exposed to current topics in effectively managing supply chains, including supply chain design, strategies, integration, visualization, analytics, risk, and mitigation. The supply chain is constantly making changes and exposed to endogenous and exogenous risks, which cause interruptions to the flow of products and a significant impact on the performance of the business. Risks spread rapidly through the chain due to the interdependence of its various nodes. Thus understanding the supply chain risks can enable organizations to take effective action to identify, assess, and mitigate risks within their end-to-end supply chain.
This course is designed to provide a comprehensive view of the nature and practice of leadership. Among the topics explored are historical, philosophical and theoretical foundations; ethics and values; power and influence; conflict management; and effective leadership in formal organizations.
This course explores advanced concepts and theories related to leadership with an emphasis on contemporary topics of leadership and factors that guide leader behavior. Students will examine classic and current scholarship to bridge theory and practice. The course focuses on critical thinking about leadership.
This course focuses on technical and visual aspects of inspecting and presenting data. Technical topics include importing data from various sources, establishing relationships between data tables, transforming data, filtering, sorting, and aggregation. Visuals will be designed to focus attention on what the data is saying, with a special focus on visuals that respond dynamically to user manipulations. Emphasis will be placed on the design/refinement cycle for visualizations.
This course allows the student to understand and demonstrate knowledge of descriptive and inferential statistics used in research, and apply their knowledge to real-world situations and research questions. Emphasis is placed on distinguishing similarities and differences among statistical tests, and recognizing the essentiality of statistics for producing and comprehending scientific research
Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.
Data mining is the study of discovering and assessing patterns, relationships and information within large data sets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.
Students will learn specialized applications of operations research to problems arising from business. These will include data envelope analysis, transportation/transshipment problems, goal programming, network models (including PERT-CPM), and capital budgeting. Other topics such as inventory models, facility location problems, etc. will be covered as time and student interest permit. Special attention will be paid to the development and analysis of models for realistic medium- to large-scale problems.
The main purpose of the capstone course is to provide the culminating, integrative curricular experience for students. The course consolidates students' learning to develop a project with knowledge gained from many areas in the MSQM. The focuses of the course are case analyses and professional development.
Integrative Capstone Experience II's main purpose is to provide a structured means for students to get hands-on experience in real-life business analytics practices. Students will apply skills and knowledge gained throughout the MSQM program, such as statistical techniques, models, and analytical decision-making that support the business-defined problems scoped collaboratively between companies and Concordia.
An introduction to the theory and practice of quantitative modeling and optimization, with applications to computer simulation and business resource management. Possible topics include linear and nonlinear programming, network analysis, game theory, deterministic and probabilistic models.