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National College Credit Recommendation Service

Board of Regents  |  University of the State of New York

UPI Study, Inc. | Evaluated Learning Experience

Math 250: Quantitative Analysis

Course Category: 

Varies (self-study, self-paced).

Various; distance learning format.

March 2021 - Present. 

Instructional delivery format: 
Traditional classroom model
Online/distance learning
Learner Outcomes: 

Upon successful completion of the course, students will be able to: acquire essential knowledge and skills for strategic decision-making and risk analysis in the business field, emphasizing the use of quantitative methods; develop proficiency in key quantitative tools, including decision trees, payback analysis, and simulations, for effective managerial decision-making; gain a comprehensive understanding of the quantitative approach to decision-making, focusing on its advantages, objectives, and application under conditions of certainty, risk, and uncertainty; master business risk management through in-depth analysis and the application of risk management strategies, integrating concepts like probability distributions and Monte Carlo simulation; and prepare for data-driven, strategic decision-making, equipping students to navigate the challenges of today's dynamic business environment.


The course is self-paced, and instruction is delivered through online video and text lessons. Students are assessed through quizzes, assignments and a proctored final exam. Major topics include  strategic decision making and risk analysis; simulation techniques in quantitative analysis; inventory management models; waiting line models and queueing theory; decision analysis for business; project planning and management; integer linear programming; linear programming; sensitivity analysis in business; linear programming applications; and distribution and network models.

Credit recommendation: 

In the lower division baccalaureate/associate degree category, 3 semester hours in Mathematics, Computer Science, Economics, Actuarial Science, Statistics, International Business, Finance, Finance and Investment, Management, Computational Finance, Financial Engineering or Data Science (1/24).