Introduction to Scientific Computing.
Instructor: Dave Feldman
College of the Atlantic
This is an introductory programming class emphasizing programming
techniques and ideas that arise in scientific computing. We will
begin with a quick overview of the python programming language,
including control statements, input/output, and user-defined
functions. The rest of the course will be structured around a series
of case studies designed to teach additional programming skills and
illustrate different ways that coding is used in the sciences and
social sciences. These case studies will include: solving systems of
ordinary differential equations, stochastic modeling, resampling and
bootstrapping, and agent-based models. If time permits, additional
case studies may include: networks, text analysis, and spatial
models. Throughout, program design and general principles for
effective scientific coding will be emphasized.
Students who successfully complete this class will gain an
understanding of the basic elements of the python programming
language as well as scipy and numpy, two packages whose use is
ubiquitous in scientific computing. Students will learn how to
develop, implement, and test code for a variety of applications across
the natural, physical, and social sciences. This class is not
recommended for students interested in a general introduction to the
principles of computer science, nor is it recommended for students
interested in applications outside of the sciences.
Evaluation will be based on weekly programming exercises.
While not strict pre-requisites, coursework in calculus or statistics
will be helpful. Prior programming experience is not necessary.
Enrollment by permission of instructor only.
Class size limited to 15. *QR* *ES*. Intermediate. No lab fee.