Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-57-53-M-6

Data Science Literacy (M, 8.0 LP, ANL)

Module Identification

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 [6] Master (General)
Language [EN] English
Module Manager
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
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
3V+3U INF-57-53-K-6
Data Science Literacy
P 84 h 156 h
ja PL1 8.0 WiSe
  • About [INF-57-53-K-6]: Title: "Data Science Literacy"; Presence-Time: 84 h; Self-Study: 156 h
  • About [INF-57-53-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
    • It is a prerequisite for the examination for PL1.

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.


  • 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

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)


Requirements for attendance of the module (formal)


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