Chair of the Department of Mathematics and Statistics: David C. Vella
Director of the Quantitative Reasoning Program: Rachel Roe-Dale
Professors: Mark Hofmann; R. Daniel Hurwitz; Mark E. Huibregtse, The Class of 1964 Term Professorship; David C. Vella
Associate Professors: Rachel Roe-Dale
Assistant Professors: Michael Lopez, Lucy Spardy
Visiting Assistant Professor: Shawn Baland, Csilla Szabo
Teaching Professor: *Rebecca Trousil
Lecturer: *Don Bunk, Megan DiMaio, *Erin Lopez
* = part time
Mathematics is an academic discipline which is fascinating to study in its own right but also has very wide-ranging applications throughout the modern world. Our faculty are all skilled and dedicated teachers as well as active scholars; we strive to make each course we offer engaging and challenging. Our graduates go on to a great variety of careers in such areas as theoretical mathematics, actuarial science, applied mathematics, teaching at various levels, and many more.
Advice for Students Beginning the Study of College Mathematics:
Students who plan to study mathematics at Skidmore should take the online Calculus Placement Exam prior to the beginning of classes (www.skidmore.edu/mcs/calcplacement.php). Based on the results of this exam, the department will recommend in which courses in the sequence the student should begin: MA 108/109 Calculus with Algebra I and II (a two-semester version of Calculus I for students who need additional pre-calculus preparation), MA 111 Calculus I, MA 113 Calculus II, MA 200 Linear Algebra, or MA 202 Calculus III.
Credit for Advanced Placement:
Students receiving a score of 4 or 5 on the Math AB AP exam will receive credit for having taken MA 111. Students receiving a score of 4 or 5 on the Math BC AP exam will receive credit for having taken MA 113. Students receiving a score of 4 or 5 on the Statistics AP exam will receive credit for having taken MS 104.
Mathematics Minor Requirements
Statistics Minor Requirements
All MA and MC courses (except MA 100) have the satisfaction of QR1 as a prerequisite.
MS 240 - Applied Regression Analysis