Vorlesung: Scientific Programming in Python - Details

Vorlesung: Scientific Programming in Python - Details

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Allgemeine Informationen

Veranstaltungsname Vorlesung: Scientific Programming in Python
Untertitel
Veranstaltungsnummer 8.3323
Semester SoSe 2021
Aktuelle Anzahl der Teilnehmenden 189
Heimat-Einrichtung LE Cognitive Science
Veranstaltungstyp Vorlesung in der Kategorie Offizielle Lehrveranstaltungen
Erster Termin Donnerstag, 15.04.2021 12:00 - 14:00
Art/Form
Voraussetzungen Basic knowledge of programming (as e.g. gained in Informatik A). Access to a personal computer with a Linux-based operating system is highly recommended. As Python has a comparatively simple syntax, coming from other programming languages such as Java or C++ should not be too big of a problem. Note however that we will only give a brief introduction to Python itself, so if you want to start with the basics, we recommend the course "Basic Programming Python".
Leistungsnachweis There will be automatically graded homeworks each week. Passing enough of these homeworks will result in a passing grade for participants (yielding 4 gradeless ECTS points). Additionally, there will be an optional graded project at the end of the semester, so that whoever wants a grade can also receive it (especially relevant for those who study under the new examination regulations and need a grade for the "Methods of Cognitive Science" module).
SWS 2
Sprache Englisch
ECTS-Punkte 4

Räume und Zeiten

Keine Raumangabe
Donnerstag: 12:00 - 14:00, wöchentlich
Freitag, 14.05.2021 12:00 - 14:00

Kommentar/Beschreibung

In this course, we will handle the tools and methods you need for Scientific Programming in Python. This course will not be taught by Kai-Uwe Kühnberger, but by Martin Pömsl, Sören Selbach and Nion Schürmeyer, students of Cognitive Science at the IKW.

The rough outline of the course is as follows: We will first give a very brief introduction to Python and tools that can be used in conjunction with Python. After that, we will delve into common Python packages used for scientific computing, such as NumPy for numerical computing, Pandas for tabular data processing and Matplotlib for visualization. The last few lectures as well as the final project topics will be guided by the interests of the participants of this course.