Spring 2024 Quantitative Literacy Courses

Honors courses in quantitative literacy focus on honing the ability to reason and solve quantitative problems applicable in various contexts, to create sophisticated arguments supported by quantitative analysis, and to communicate the findings to a broad audience. Remember that…

  • All Honors students are required to complete at least one Honors Quantitative Literacy course.
  • Alternatively, the Honors Quantitative Literacy requirement can be satisfied with MATH 120 (or AP credit for MATH 120).
  • Quantitative Literacy courses count towards the 22 HONS credit requirement.
  • Students may take additional Quantitative Literacy courses as an Honors elective.
  • The prerequisite(s) for Honors Quantitative Literacy courses vary by course.

HONS 216 – Conceptual Tour of Contemporary Mathematics
Instructor: James Young
TR 10:50 – 12:05 p.m.

This course will highlight mathematics as a network of intriguing and powerful ideas, not a dry formula list of techniques. Emphasis will be placed on conceptual, non-technical understanding of current developments in higher-level mathematics, and how these concepts and results are intertwined and employed in other areas outside mathematics.

Prerequisite(s): MATH 116 or MATH 120 or equivalent; or permission of instructor

This course counts towards the College's math/logic general education requirement

HONS 217 – Honors Statistics
Instructor: Martin Jones
MWF 9:00 – 9:50 a.m.

Honors Statistics introduces students to the world of stochastic phenomena and modeling including probability, statistical inference, and stochastic processes. The course covers the axioms of probability and fundamental laws of probability including the Law of Large Numbers, the Central Limit Theorem, conditioning, and Bayes Theorem. Using probability theory the course develops statistical inference procedures including point estimation, confidence intervals, hypothesis tests, and multiple linear regression. Elementary stochastic processes are covered via discrete-time Markov chains with applications. Real world examples and real data will be used to demonstrate the power and utility of stochastic modeling and statistical inference across a wide variety of disciplines.

Prerequisite(s): MATH 116 with a C- or better or MATH 111 or MATH 120; or permission of instructor

This course counts towards the College's math/logic general education requirement

*Please note that Spring 2024 course offerings are tentative, and are subject to change