Modulhandbuch (Module manual)

M.184.4454 R for Data Science
(R for Data Science)
Koordinator (coordinator): Prof. Dr. Yuanhua Feng
Ansprechpartner (contact): Prof. Dr. Yuanhua Feng (yuanhua.feng[at]
Sebastian Letmathe (lettron[at]
Dominik Schulz (dominik.schulz[at]
Credits: 5 ECTS
Workload: 150 Std (h)
Semesterturnus (semester cycle): SoSe
Studiensemester (study semester): 1-4
Dauer in Semestern (duration in semesters): 1
Lehrveranstaltungen (courses):
Nummer / Name
(number / title)
(contact time)
Status (P/WP)
(group size)
a) K.184.44541 / R for Data Science Vorlesung P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Empfohlene Voraussetzungen (prerequisites):

It is recommended that you have visited one of the following courses:

  • ​W4479 Econometrics
  • W4451 Financial Econometrics and Quantitative Management
  • Or that you have prior experience with the programming language R
  • Inhalte (short description):

    ​Part I of this course focuses on data visualization, data wrangling and data analysis by means of modern R-packages  as well as programming of functions, algorithms etc..

    The topics of Part II are more advanced. The students will learn how to build own R packages, work with R Markdown (a powerful tool for combining analysis, text editing and documenting code), create interactive applications by means of the "shiny" framework, manage data with R and SQL and learn how to use the programming language Python from within RStudio (an IDE for R).

    Lernergebnisse (learning outcomes):
    Fachkompetenz Wissen (professional expertise):
  • learn how to appropriately visualize data
  • learn how to prepare data for further analysis
  • learn how to correctly analyse data
  • learn how to programm functions, algorithms​ etc.

  • Fachkompetenz Fertigkeit (practical professional and academic skills):
  • are able draw correct conclusions from data by means of visual and numerical analysis
  • are able to create own R-packages
  • are able to use R Markdown (for scientific work and code documentation)
  • are able to manage data by means of SQL (used from within RStudio)
  • are able to use the programming language Python from within RStudio​

  • Personale Kompetenz / Sozial (individual competences / social skills):
  • ​work together in teams 
  • actively discuss coding tasks
  • present own results/solutions​

  • Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
  • indepently conduct empirical studies by means of R
  • indepently create R packages
  • safe handling with other programming languages (Python and SQL)
  • critically analyse data​

  • Prüfungsleistungen (examinations)
    Art der Modulprüfung (type of modul examination): Keine Modulprüfung
    Art der Prüfung
    (type of examination)
    a) Projektarbeit 10 - 15 pages 50.00 %
    b) Projektarbeit 10 - 15 pages 50.00 %
    Studienleistung / qualifizierte Teilnahme (module participation requirements)
    Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
    Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
    Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist
    Gewichtung für Gesamtnote (calculation of overall grade)
    Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)
    Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
    M.Sc. IBS, M.Sc. BWL, M.Sc. International Economics and Management, M.Sc. Management, M.Sc. Management Information Systems, M.Sc. Taxation, Accountingand Finance, M.Sc. Winfo, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik
    M.Sc. International Economics and Management
    Umfang QT (participation requirements):
    Lernmaterialien, Literaturangaben (learning material, literature):
    Main references:

    Wickham and Grolemund (2017). R for Data Science - Import, Tidy, Transform, Visualize, and Model Data. O'Reilly.

    Wickham (2019). Advanced R (2nd ed.). Chapman & Hall / CRC.

    Wickham (2015). R Packages: Organize, Test, Document, and Share Your Code. O’Reilly.
    Xie, Allaire and Grolemund (2018). R Markdown: The Definitive Guide. Chapman & Hall / CRC.
    Wickham (2021). Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R. O’Reilly.
    Mailund (2017). Beginning Data Science in R - Data Analysis, Visualization, and Modelling for the Data Scientist. APRESS.
    Baumer et al. (2021). Modern Data Science with R. CRC Press.
    Zumel and Mount (2020). Practical Data Science with R (2nd ed.). Manning.
    Peng (2015). R Programming for Data Science. A Leanpub Book.
    Teilnehmerbegrenzung (participant limit):
    Sonstige Hinweise (additional information):

    ​keine Teilnehmerbeschränkung

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