The course lectures cover the theoretical basis of the following subject areas:
• Essential linear algebra (matrices, eigenvalues, linear systems of equations)
• Essential calculus (differentiation, integration, Taylor series)
• Essential statistics (error analysis, correlation, significance)
• Essential optimization (linear and nonlinear regression, parameter estimation,
gradient methods)
• Essential differential equations (ordinary and partial differential equations, phase
diagrams)
In the example classes students will learn how to apply this knowledge both analytically
and numerically. In order to facilitate the latter, students will learn the basics of the Python programming language and how to use Python to solve real-world problems
from the course’s topic areas
Outcome:
Basic knowledge in mathematical methods for data analysis and their application using
the Python programming language.
