Thomas Prevendoski Headshot

Thomas Prevendoski

Principal Lecturer, Mathematics

School of Mathematics and Statistics
College of Science

Office Hours

Monday 10 AM to noon Tuesday 8 AM to 9:30 AM Wednesday 10 AM to noon Thursday 8 AM to 9:30 AM

Office Location

Thomas Prevendoski

Principal Lecturer, Mathematics

School of Mathematics and Statistics
College of Science

Education

BS, Rochester Institute of Technology; MS, University of Arizona

Currently Teaching

MATH-104
3 Credits
This course provides an exploration of assorted mathematical concepts by using a hands-on approach. Topics will be selected from a wide array of fields to show the presence and importance of mathematics in everyday life.
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-161
4 Credits
This course is an introduction to the study of differential and integral calculus, including the study of functions and graphs, limits, continuity, the derivative, derivative formulas, applications of derivatives, the definite integral, the fundamental theorem of calculus, basic techniques of integral approximation, exponential and logarithmic functions, basic techniques of integration, an introduction to differential equations, and geometric series. Applications in business, management sciences, and life sciences will be included with an emphasis on manipulative skills.
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.