Calculus I
Comparable to a semester-long, college-level calculus I course, this class will give students an appreciation of the two principal divisions of calculus - differential calculus, connecting slope and rate, and integral calculus, connecting area and accumulation.
Calculus I and II
This course covers the material typically presented over two semesters of college-level calculus through a spiraled, problem-based methodology. Students develop a deep understanding of the core principles of differential and integral calculus, along with their broad applications in science and engineering.
Statistical Reasoning
Statistical Reasoning invites juniors and seniors to explore how data can be used to understand complex questions, model uncertainty, and make evidence-based decisions. Through inquiry-driven lessons and simulations, students learn to analyze data, build statistical models, and communicate their reasoning with clarity and precision.
Probability and Random Variables
Students engage in a proof-based study of the mathematical theory behind random variables, including set theory and combinatorics. They calculate expected values, variance, and moment generating functions for both discrete and continuous variables.
Semester Courses (each will be offered in either the Fall or Spring semester):
Calculus II
This advanced course serves as an opportunity for students who have completed AS Calculus I to continue their study of calculus.
Multivariable Calculus
Students explore multiple coordinate systems and examine the graphs of surfaces in three dimensions, applying the tools of calculus to these functions. Students investigate how derivatives and integrals extend to higher dimensions.
Linear Algebra
Students solve systems of linear equations using matrices and explore vector spaces, eigenvalues, and orthogonality. They practice the art of writing clear, analytical, and deductive proofs to support the theorems of linear algebra.
Statistical Modeling
This advanced, fast-paced course introduces statistics through a computational lens, using R to emphasize data exploration, modeling and modern approaches to statistical thinking. Emphasizing simulation-based inference, randomization and bootstrapping, the course is designed for students with strong mathematical backgrounds and develops fluency in applying statistics to real-world data across disciplines.