Modul


Allgemeine Informationen
Tools and Programming Languages for Data Science
Tools and Programming Languages for Data Science
MADS-TPDS
ToolsProgLan-01-MA-M
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Wintersemester 2022/23
1 Semester
In der Regel jedes Semester
Englisch
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)
Studiengang Vertiefungsrichtung Schwerpunkt Modulart Fachsemester
M.Sc. - DS - Data Science Pflichtmodul

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
Students know
- the foundations of the programming language Python
- standard workflow and corresponding programming processes in data science projects
- tools and practices that ensure reproducibility of results and reusability of code
Students are able to
- acquire, process, clean, analyse and visualize data
- prepare data for downstream data science tasks
- document and present their results and approach
Students are able to
- communicate approach and results to technical and non-technical audiences
- work in teams on programming tasks using version control systems
- give and receive critique in a professional manner
Students are able to
- leverage relevant literature
- give and accept professional feedback
Angaben zum Inhalt
Python Foundations
- data types
- functions
- control flow
- comprehensions
- generators
- tooling (IDEs, Notebooks, virtual environments)

Python Data Science
- Data Science Packages (NumPy, Pandas, Matplotlib, ...)
- Reading and writing data
- Cleaning and exploration data
- Visualizing data

Git and GitHub
- VanderPlas: A Whirlwind Tour of Python. O'Reilly, first edition. Available online: https://jakevdp.github.io/WhirlwindTourOfPython/
- VanderPlas: Python Data Science Handbook. O'Reilly, first edition. Available online: https://jakevdp.github.io/PythonDataScienceHandbook.
- McKinney: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly, second edition.
Lehrformen der Lehrveranstaltungen
Lehrform SWS
Lehrvortrag + Übung 4
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Prüfungsform Dauer Gewichtung wird angerechnet gem. § 11 Absatz 2 PVO Benotet Anmerkung
Portfolioprüfung 100 %
Sonstiges
- basic Python programming skills (e.g. by participating at the Pre-Course Programming)