Fall 2022 Quantitative Literacy Courses

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. Note that…

  • All Honors College 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)
  • 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
Professor James Young
TR 3:05 – 4:20 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 General Education Mathematics/Logic requirement

HONS 217: Honors Statistics
Professor Bo Kai
TR 3:05 – 4:20 p.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. This is the Honors course version of MATH 250. Students may not receive credit for both.

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

This course counts towards the College’s General Education Mathematics/Logic requirement


*Please note that Fall 2022 course offerings may be subject to change