Module


General information
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
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - DS - Data Science Pflichtmodul

Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
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
Content information
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.
Teaching formats of the courses
Teaching format SWS
Lehrvortrag + Übung 4
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting wird angerechnet gem. § 11 Absatz 2 PVO Graded Remark
Portfolioprüfung 100 %
Miscellaneous
- basic Python programming skills (e.g. by participating at the Pre-Course Programming)