Mathematical Sciences for Quantitative Reasoning
In our increasingly data-rich world, students and working professionals who possess quantitative reasoning skills are better prepared to succeed in their academic professional, civic, and personal lives. Whether you are preparing for graduate-level coursework in a STEM-based Master’s program or seeking to improve your competency for one of the many in-demand 21st century careers, the Mathematical Sciences for Quantitative Reasoning (MSQR) course is for you.
This course offers foundational training in mathematical sciences, which provides the academic underpinning for quantitative reasoning skills that enable data-based decision-making in an applied context. Individuals who possess such training will be able to extract information from data, create well-reasoned arguments supported by quantitative evidence, and share those arguments using verbal, written, and visual communication methods.
In This Course, You Will...
This course is designed for learners aiming to improve their quantitative reasoning skills by building their foundational knowledge in the mathematical sciences and applying that learning to real-world problems. It is ideal for learners aiming to prepare for graduate studies as well as for industry professionals wanting to apply new skills to their professional and personal lives.
The course begins with topics in algebra, linear algebra (including matrices and vector spaces), and calculus (including functions, limits, derivatives, and integrals). Students will then learn about descriptive statistics, graphical and numerical summaries, probability, the theory of sampling distributions, linear regression, analysis of variance, confidence intervals, and hypothesis testing. Throughout the course, real-world examples and practical applications will be shared from industry to illustrate how quantitative reasoning is used to improve decision-making. Students will be given the opportunity to work with data to build applied quantitative reasoning skills.
By the end of the course, you will be able to:
- Model statistical and financial concepts in Excel and VBA.
- Readily explain statistical concepts such as maximum likelihood, hypothesis testing, linear regression and its assumptions, logistic regression and its construction.
- Describe the quality of a classification model in terms of standard terminology such as sensitivity, specificity, accuracy, etc.
- Use linear regression to model data and assess the quality of the fit.
- Construct probability distributions and test hypotheses.
- Use matrix algebra to model the states and variability of systems.
- Price and model the volatility of financial assets.
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As admissions to our program are highly competitive, we encourage you to apply as soon as possible. Learn more about the application process, deadlines, and requirements.Learn more