# Computational Mathematics

## Course Description

A modern mathematician needs solid programming and data analysis skills to solve real-world problems in finance, medicine, technology or science. Working with data, solving mathematical problems computationally, and visualizing results are important skills to combine with mathematical knowledge. Therefore, the aim of this course is to provide a link between mathematics and computer science for the mathematics students. We will offer an introduction into Python programming language and its applications to mathematics and data analysis. Specifically, we will focus on packages for scientific computing and linear algebra (NumPy, SciPy), data handling (Pandas), visualisation (Matplotlib) and symbolic computation (SymPy).

## 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: Tue 15.15-16.00; Tue 16.15-17.00; Wed 15:00-15:45; Wed 16:00-16:45 (by Laura Thesing and Katharina Bieker; in german)

Exercise Class: Wed 13:00-13:45; Wed 14:00-14:45; Thurs 11:00-11:45; Thurs 12:00-12:45 (by Mariia Seleznova, in english)

Office Hours: Thurs 16:00-17:00, Akademiestraße 7, Room 513

## 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: https://moodle.lmu.de/enrol/index.php?id=29543 ("Computergestützte Mathematik (Thesing, Bieker)").

The access key is CM23.