COMPUTATIONAL MATHEMATICS (6 SCU)

Learning Outcomes:

Upon successful completion of this course, students are expected to be able to recognize numerical computation and error propagation in approximated solutions, apply a computational method for visualizing the central tendency and distribution of a dataset, apply a computational method to approximate the function with a series, apply a computational method to obtain an approximation solution for statistical or calculus problems, and be able to apply a computational method to solve problems in a student-selected domain.

Topics:

This course provides an introduction to a numerical computation method for computing an approximate solution to solve problems using statistical and calculus techniques. The material is presented using a problem-oriented perspective with examples from the applied sciences. Topics to be covered include: : Introduction to Python, Data Visualization, Statistical Analysis, Linear Algebra, Taylor Series, Initial Value Problems, Numerical Integration, Linear Regression, Interpolations, Finding Roots of Equations, Numerical Differentiations, Two-Point Boundary Value Problems, Optimization, and project presentation.