Physics 211: Computational Method in the Physical Sciences (Spring 2026)
This course provides an overview of commonly used computational methods and their applications to physics problems. Using the Python programming language, students begin with learning how programs send instructions to computers, move on to simple data visualization, error analysis and uncertainty in computational calculations, and then progress to numerical integration and differentiation, machine learning and stochastic methods. In each case, students examine the method’s applications to relevant physics scenarios. This course is project-based, with multiple short projects throughout the semester intended to build the skills and generate a set of modules that can be used as part of a final project applying a computational method to an appropriate physics problem of the student’s choice.
Physics 210: Mathematical Methods of Physical Sciences and Engineering (Spring 2026)
This course covers a variety of math topics of particular use to physics and engineering students. Topics investigated in class include ordinary differential equations, linear algebra, Fourier analysis, partial differential equations and a review of multivariate calculus, with particular focus on physical interpretation and application.
Statistical and Data Sciences 271: Programming for Data Science in Python (Fall 2025)
This course covers the skills and tools needed to process, analyze and visualize data in Python and work on collaborative projects. Topics include functional and object oriented programming in Python, data wrangling in Pandas, visualization in Matplotlib in seaborn, as well as creating a reproducible workflow: debugging, testing and documenting programs, and effectively using version control. The major goal for the course is to create a viable, open-source Python package like those in the Python Package Index (PyPI).
Statistical and Data Sciences 291: Multiple Regression (Fall 2025)
Theory and applications of regression techniques: linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences.
