This is the main course website for the lecture Data Modeling and Knowledge Generation in winter term 2023/24 at University of Bayreuth.
Jump to the schedule to access explanations and materials!
- 08. December 2023: First lecture regarding algorithms will be about clustering!
- 01. December 2023: Second practical is online. Today we will be working with git!
- 01. December 2023: Now to Data and Knowledge Management!
- 24. November 2023: Jump to the schedule to view the slide deck and the presentation!
- 17. November 2023: This week’s lecture is about databases.
- 10. November 2023: This week’s topic: Data Modeling. See the schedule for the recording of the lecture and accompanying materials.
- 03. November 2023: First practical finished! Yay! Download slides and material from the schedule!
- 03. November 2023: Lecture about the shape of data is online!
- 27. October 2023: Digitalization will be the topic of 27th of October!
- 20. October 2023: Lecture starts and introductory slides are online! See the schedule to download the slides. Make sure to prepare next weeks session before class!
Recordings of the lecture are available online but require an account of UBT.
Data models represent the real world in the analysis process, they act as their placeholder, so to speak. As such, they create their own reality for the analyses. The formulation of data models is always subject to conscious and unconscious selection and transformation decisions. These decisions implicitly influence the way algorithms and analysts understand and process the real world. At the same time, data models act as blueprints for a real world that comes after analysis. Finally, analysis results are produced and evaluated with the help of data models and communicated as new knowledge. The decisions mentioned above therefore have far-reaching implications for the expected results and the knowledge that can be gained from these results. This dual role of description and prescription opens up a field of tension for the analysis process in interdisciplinary research as well as in numerous business areas that make use of "data driven decision making", for example. Only when the data model, algorithm and results are viewed as a holistic unit of an analysis process can reliable knowledge be gained from data.
In this course, different methods for data analysis and knowledge generation will be presented - including methods from the fields of machine learning, data mining, text mining, social network analysis and information visualization. These methods, which are currently widely used in science, business and beyond, bring about different requirements for the modelling of data. These requirements are viewed critically. The implications for the expected results and the knowledge derived from them are explicitly stated.
Students will learn different methods for data analysis and knowledge generation - including methods from the fields of machine learning, data mining, text mining, social network analysis and information visualization. Students become aware of the requirements for the required data models that the different analysis methods entail. Students know how to critically question data analyses, how to specify the implicit modelling decisions and how to evaluate analysis results always against the background of these decisions.
In this section, you will find the lectures together with materials, recordings, self-tests, and downloadable slides.
|Definition of data, datafication, and usage of data. Motivation for the lecture.|
The shape of data¶
|Logical modeling, relational databases, RDF, Graph databases|
|Metadata, Ontologies, Knowledge Graphs|
|Data Management, Knowledge Management, Long-term archiving||
|Working with git||
|Unsupervised Learning: Clustering, Silhouettes, Curse of Dimensionality||
|Session was a lecture|
|Session was a practical|
|Find a video here|
|Find slides here|
|Find code material here|
|Find external material here|
|This session has a self assessment quiz attached|
|This session has a script with gap text attached|
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