This is the main course website for the lecture Data Modeling and Knowledge Generation in winter term 2021/22 at University of Bayreuth.
- 04. February 2022: The last practical is about visualizing data - Download the material here!
- 01. February 2022: Karl Popper, Bayesian Statistics, and Machine Learning.
- 25. January 2022: Important: visualizing your findings! This will be today’s topic.
- 21. January 2022: Fifth practical deals with handling text data in R. Download the material here!
- 18. January 2022: Today we talk about modeling text data
- 14. January 2022: A practical covering machine learning in R. Jump right in!
- 11. January 2022: Last (theoretical) session about machine learning today.
- 21. December 2021: Supervised Learning will be today’s topic!
- 14. December 2021: Today will be about clustering!
- 07. December 2021: With lecture number 8 we’ll start learning about methods
- 30. November 2021: Jump to the schedule to download the latest slide deck!
- 26. November 2021: Third practical is online. Today we will be working with git!
- 23. November 2021: Lecture number six is online!
- 16. November 2021: Lecture number five is about databases.
- 12. November 2021: Second practical done. That’s the first simple data inspection and cleaning!
- 09. November 2021: Lecture number four is online!
- 02. November 2021: Slides for third lecture are online.
- 29. October 2021: First practical finished! Yay! Download slides and material from the schedule!
- 26. October 2021: Slides for second lecture are online.
- 19. October 2021: Lecture starts and introductory slides are online! See the schedule to download the slides.
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.
|Session was a lecture|
|Session was a practical|
|Find slides here|
|Find code material here|
|Find external material here|
|This session has a self assessment quiz attached|
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