Computational Mathematics

Course Description

A modern mathematician needs solid programming and data analysis skills to solve real-world problems in finance, medicine, technology, and science. Working with data, solving mathematical problems computationally, and visualizing results are essential skills that complement mathematical knowledge.
Therefore, the aim of this course is to bridge the gap between mathematics and computer science for mathematics students. We will provide an introduction to the Python programming language and its applications in mathematics and data analysis. Specifically, we will focus on packages for scientific computing and linear algebra (NumPy, SciPy), data handling (Pandas), visualization (Matplotlib), and symbolic computation (SymPy). Finally, we will discuss the opportunities and challenges arising from the use of large language models (LLMs) and coding agents.

Schedule and Venue

The course consists of a lecture (1 SWS) and an exercise class (1 SWS). You can attend the lecture and the exercise class during any of the four available time slots (only choose one lecture and one exercise class):

Lecture: tba.

Exercise Class: tba
Office Hours: tba

Requirements

The course is targeted at Bachelor students of mathematics and financial mathematics, as well as Lehramt students. Prior knowledge in Analysis I,II and Linear Algebra is recommended. Basic programming skills (e.g. from Programmieren I lecture) are an advantage. However, we will give an introduction to basic programming concepts and Python syntax in the beginning of the course.

Registration

Please register for the course on the moodle page: tba

Note that you need to register on Moodle to receive a CIP account necessary for this course.