Fachbereich 6 Mathematik/Informatik

Institut für Informatik


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Scientific programming in Python

8.3323

Dozenten

Beschreibung

In this course we will cover the tools and methods used to solve data science problems with a focus on understanding and implementing the algorithms covered. It will be run by Brian Lewis and Patrick Faion, both master students.

From the data-side we will give you some theoretical background of what you can do with data. This will cover statistical and mathematical methods for analysis and methods of visualization, but we will try to keep it simple and understandable. We plan to give you examples from different areas of CogSci, e.g. textual data, statistical data, time-series (EEG and such) and give you background knowledge where needed.

From the programming side we plan to get everyone quickly into Python, but you should have some minor experience with programming, Info-A will be ok. We will quickly go over the standard concepts of programming and how they work in python, like variables and loops, but later on also dig into some python-specific features and look at more advanced topics that can be useful in terms of data analysis. You will learn how to write code for data analysis, even without using existing libraries. This will give you a chance to learn how to create custom code solutions for science problems, which is often necessary in practice. We will still cover the commonly used libraries for these tasks as well.

Since this is the first time that this course will run, we have only a rough schedule, and can adapt the content also to your needs throughout the semester.

The course does not give you a grade, but only grade-less ECTS, thus you can only use it for your optional module. We will have no final exam and no "grading" of the homework. The "homework" will be an offer from us to practice the methods you learn in the lecture. Still you will have to complete a certain percentage of the homework, in order to earn the ECTS in the end.

There is a tutorial every week, where you can ask for our help, if you struggle with the homework. Attendance is not mandatory, also not in the lecture.

Weitere Angaben

Ort: 93/E33: Mi. 12:00 - 14:00 (13x), 93/E06: Do. 12:00 - 14:00 (13x)
Zeiten: Mi. 12:00 - 14:00 (wöchentlich) - Tutorial, Ort: 93/E33, Do. 12:00 - 14:00 (wöchentlich) - Lecture, Ort: 93/E06
Erster Termin: Do , 06.04.2017 12:00 - 14:00, Ort: 93/E06
Veranstaltungsart: Seminar (Offizielle Lehrveranstaltungen)

Studienbereiche

  • Cognitive Science > Bachelor-Programm
  • Cognitive Science > Master-Programm