- data cleaning
- operationalization of social concepts
- requirements of selected methods of machine learning (linear regression, logistic regression, decision trees & random forests, cluster methods)
- theoretically guided selection of quality and fairness metrics
- identification of possible limitations in the development of algorithmic decision systems with learning components
- ethical and legal aspects of data science projects
Data Science Literacy (M, 8.0 LP, ANL)
|Module Number||Module Name||CP (Effort)|
|INF-57-53-M-6||Data Science Literacy||8.0 CP (240 h)|
|CP, Effort||8.0 CP = 240 h|
|Position of the semester||1 Sem. in WiSe|
|Level|| Master (General)|
|Area of study||[INF-ALG] Algorithmics and Deduction|
|Reference course of study||[INF-88.79-SG] M.Sc. Computer Science|
|Livecycle-State||[ANL] Start-Up Period|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Data Science Literacy
|P||84 h||156 h||
Examination achievement PL1
- Form of examination: written or oral examination
- Examination Frequency: each winter semester
- Examination number: 65754 ("Data Science Literacy")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
After successfully completing the module, students will be able to
- understand data science papers able to evaluate their content,
- select appropriate machine learning methods to solve the problem,
- identify potential limitations and to explain processes for dealing with these constraints (communication & documentation).
- McCallum, Q. (2012). Bad data handbook . O'Reilly Media, Inc.
- Zweig, K. A. (2016). Network analysis literacy: a practical approach to the analysis of networks . Springer Science & Business Media.
Requirements for attendance of the module (informal)None
Requirements for attendance of the module (formal)None
References to Module / Module Number [INF-57-53-M-6]
|Course of Study||Section||Choice/Obligation|
|[INF-88.79-SG] M.Sc. Computer Science||[Specialisation] Specialization 1||[WP] Compulsory Elective|