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| Detailinformationen |
| Quellcurriculum |
Masterstudium Digital Energy Solutions 2026W |
| Lernergebnisse |
Kompetenzen |
| (*)Students can perform descriptive and inferential statistical analyses in Python, including data visualization, probability calculations, and univariate, bivariate, and multivariate methods. They can apply hypothesis tests, correlation analysis, linear and multiple regression with diagnostics, single- and multi-factor ANOVA, principal component analysis (PCA), and clustering. Students are familiar with introductory statistical experimental design, can create and analyze simple experiments, and can visualize and interpret different data types and probability distributions, fit models, and evaluate model assumptions.
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| Fertigkeiten |
Kenntnisse |
(*)- Solve basic probabilistic problems, particularly applications related to geometric, binomial, normal distributions, and Poisson distribution functions (k3).
- Create descriptive summaries of datasets using appropriate distribution indicators, tables, and graphs, and interpret the results (k3, k4).
- Construct confidence intervals and conduct hypothesis tests for one- and two-sample problems, selecting the appropriate method (k3, k4).
- Fit and interpret simple and multiple linear regression models, perform diagnostic checks, and assess model quality (k3, k4, k5).
- Apply single-factor and multi-factor ANOVA to analyze data, and interpret the results (k3, k4, k5).
- Perform principal component analysis (PCA) and clustering for multivariate data exploration (k3, k4, k5).
- Understand the principles of simple statistical experimental design, create basic experimental designs, and understand their areas of application and limitations (k2, k3).
- Use Python (or R) to solve the statistical problems discussed in class (k3)
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(*)- Fundamental axioms and rules of probability theory.
- Probability mass functions, moments, and areas of application of basic discrete probability distributions such as binomial, geometric, and Poisson distributions.
- Probability density functions, moments, and areas of application of basic continuous probability distributions such as normal and exponential distributions.
- Key location and dispersion indicators for summarizing descriptive data.
- Common graphical data representations such as histograms, box plots, scatterplots, and probability plots.
- Confidence intervals and hypothesis tests for one-sample and two-sample problems.
- Model fitting, analysis, diagnostics, and assessment of fit for simple and multiple linear regression.
- Model fitting, analysis, and assessment of fit for analysis of variance (ANOVA) with one or more factors.
- Fundamentals of multivariate analysis, including principal component analysis (PCA) and clustering methods for data exploration.
- Principles of simple statistical experimental design, creation of basic experimental designs, and analysis of experimental results.
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| Beurteilungskriterien |
(*)Written final exam, Weekly exercises, Mid-term project, Presentation
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| Lehrmethoden |
(*)Lecture using slides, discussion of homework examples
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner's Guide to Advanced Data Analysis^
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| Lehrinhalte wechselnd? |
Nein |
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