NWNR100015 Statistics and data science exercises (LBT)

Type
course with continuous assessment
Semester hours
3
Lecturer (assistant)
Steiner, Nora Anais , Kellner, Maximilian , Pils, Vera , Kochmann, Sven , Ndongala, Tracy , Scharl-Hirsch, Theresa , Ferring, Clara
Organisation
Statistics
Offered in
Sommersemester 2026
Languages of instruction
Deutsch
Content

- descriptive statistics (graphical methods, characteristics)
- applied probability (probability, random variate and its moments, distributions, probability plot, multidimensional random variates, central limit theorem)
- basics of statistical analysis (parameter estimation, hypothesis testing)
- methods for normal distribution
- analysis of variance (one-way ANOVA, two-way ANOVA)
- regression and correlation analysis (correlation coefficient, simple linear regression analysis)
- nonparametric methods (chisquare test, Kolmogorov-Smirnov test, Wilcoxon signed rank test, Wilcoxon rank test, Kruskal-Wallis test, sign test)
- contingency tables
- quality assurance
The data science part contains
- Fundamentals of programming (with R Studio), computational thinking
- Data acquisition using examples of various data sources and data types in coordination with applied modules of the UIW program
- Data preparation
- Data visualization and interpretation
- Creation of simple scientific reports
- Data journalism (explaining data in an easily understandable way and presenting it as a seminar paper in the form of a mini-article with selected graphics)
Previous knowledge expected


Objective (expected results of study and acquired competences)

At first the student should realize the uncertainty when describing natural, technical or socio-economical phenomena and methods should be offered to him for modelling (applied probability). The discussed models and methods for describing existing or observed data, for estimating parameters and for testing hypothesis on models and parameters should help the student to handle and analyse data, practical questions and problems in a senseful statistical way for his own (also using appropriate statistical software) and to judge statistcal results in a correct way.
Knowledge:
Students can describe important data sources, explain their basic data structures, and outline how to efficiently access, prepare, structure, and analyze them for data analysis. They can describe basic programming structures and know how to apply them for data preparation, analysis, and visualization. They are capable of professionally describing the process of analysis and documentation.
Skills:
Students can create simple programs and algorithms in the R programming language. This enables them to import, prepare, analyze, visualize, and document larger data structures.
Professional/Occupational Competencies:
Students are capable of independently collecting the required data in a statistically meaningful way, preparing it, and scientifically documenting the analyses in terms of the algorithms, methods, and results used.
Personal Competencies:
Students can communicate in a way that is understandable to non-experts.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.