NWNR100399 Data science (AW)

Type
course with continuous assessment
Semester hours
1.5
Lecturer (assistant)
Laa, Ursula , Ortega Menjivar, Lena
Organisation
Statistics
Offered in
Sommersemester 2026
Languages of instruction
Deutsch
Content

‐ Fundamentals of programming (with R Studio), computational thinking
‐ Data acquisition using various data sources and data types in coordination with applied modules of the AW degree program
‐ Data preparation
‐ Data visualization and interpretation
‐ Creation of simple scientific reports
‐ Data journalism (explaining data in an easy-to-understand way and presenting it as a seminar paper in the form of a mini-article with selected graphics)
- Nichtparametrische Verfahren (Wilcoxon-Vorzeichen-Rangtest, Wilcoxon-Rangsummen-Test, Kruskal-Wallis-Test)
- Kontingenztafeln
Previous knowledge expected

Basic Mathematics (introductory course)
Objective (expected results of study and acquired competences)

Knowledge:
Students can describe important data sources, explain their basic data structures, and outline how they can efficiently access, prepare, structure, and evaluate them for data analysis. They can describe basic program structures and know how to apply them for data preparation, analysis, and visualization. They are able to describe the process of analysis and documentation in a technically correct manner.

Skills:
They can create simple programs and algorithms in the R language. This enables them to import, prepare, analyze, visualize, and document even large data structures.

Technical/professional skills:
They are able to independently collect and prepare the necessary data in a statistically meaningful way and to document the analyses scientifically and correctly with regard to the algorithms and methods used and the results obtained.

Personal skills:
Ability to communicate scientific content in a way that is understandable to laypeople.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.