Susan Bateman Headshot

Susan Bateman

Visiting Lecturer

School of Mathematical Sciences
College of Science

585-475-5184
Office Hours
Mon/Wed 12:15-1:45 Thurs 12-1 And by appointment
Office Location

Susan Bateman

Visiting Lecturer

School of Mathematical Sciences
College of Science

585-475-5184

Currently Teaching

MATH-111
3 Credits
This course provides the background for an introductory level, trigonometry-based calculus course. Topics include functions and their graphs, with an emphasis on functions that commonly appear in calculus including polynomials, rational functions, trigonometric functions, exponential functions, and logarithmic functions. The course also includes the analytic geometry of conic sections. One hour each week will be devoted to a collaborative learning workshop.
MATH-172
3 Credits
This is the second course in three-course sequence (COS-MATH-171, -172, -173). The course includes Riemann sums, the Fundamental Theorem of Calculus, techniques of integration, and applications of the definite integral. The techniques of integration include substitution and integration by parts. The applications of the definite integral include areas between curves, and the calculation of volume.
MATH-171
3 Credits
This is the first course in a three-course sequence (COS-MATH-171, -172, -173). This course includes a study of functions, continuity, and differentiability. The study of functions includes the exponential, logarithmic, and trigonometric functions. Limits of functions are used to study continuity and differentiability. The study of the derivative includes the definition, basic rules, and implicit differentiation. Applications of the derivative include optimization and related-rates problems.
MATH-189
1 - 3 Credits
This is a course suitable for first-year students that covers topics not currently offered in the curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures.
STAT-145
3 Credits
This course introduces statistical methods of extracting meaning from data, and basic inferential statistics. Topics covered include data and data integrity, exploratory data analysis, data visualization, numeric summary measures, the normal distribution, sampling distributions, confidence intervals, and hypothesis testing. The emphasis of the course is on statistical thinking rather than computation. Statistical software is used.