All Honors College students are required to complete at least one Honors Quantitative Literacy course. Quantitative Literacy courses count towards the 25 HONS credit requirement, and students may take additional Quantitative Literacy courses as an Honors elective. Alternatively, the Honors Quantitative Literacy requirement can be satisfied with MATH 120 (or AP credit).
HONS 115 Honors Calculus I (Professor Alex Kasman)
Four credits
This course will introduce differential and integral calculus of functions of one variable, addressing both the historical context and the modern applications of the subject. Special emphasis will be given to communication skills, developing the students’ ability to understand and clearly convey quantitative/mathematical information. This is the Honors course version of MATH 120. Students may not receive credit for both.
This course counts towards the College’s General Education Mathematics/Logic requirement
Teaching Format: The majority of class meetings will occur in-person, as able
HONS 216 Conceptual Tour of Contemporary Mathematics (Professor James Young)
Three credits
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.
This course counts towards the College’s General Education Mathematics/Logic requirement
Prerequisite(s): MATH 105 or MATH 120 or equivalent; or permission of instructor.
Teaching Format: Synchronous Online class meetings with in-person testing: 1) Classes will meet online via Zoom at the regularly scheduled class times. Instructor will lecture at a blackboard, answer questions, and encourage and expect class participation. 2) All exams will be administered and proctored as paper exams to be taken in-person during regular class time or final exam time.
HONS 217 Honors Statistics (Professor Bo Kai)
Three credits
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 course counts towards the College’s General Education Mathematics/Logic requirement
Prerequisite(s): MATH 105 with a C- or better or MATH 111 or MATH 120 or permission of the instructor.
Teaching Format: The majority of class meetings will occur in-person, as able
*course offerings and teaching format subject to change