Skip to main content
SpringerLink
Log in

Data Science: a proposal for a curriculum

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

We define Data Science as the combination of statistical and computational data analytic approaches. We argue that only this combination allows to tackle the many problems occurring in today’s Big Data era. We outline a possible curriculum, which focuses on both statistics and computer science aspects of data analytics. The proposed curriculum is implemented in the Data Science master program run at the University Munich (www.datascience-munich.de). We also argue that data ethical aspects as well as practical and communication skills are essential in a modern Data Science study program.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. https://en.wikipedia.org/wiki/Data_science assessed 24.2.2017.

References

  1. Cleveland, W.S.: Data science: an action plan for expanding the technical areas of the field of statistics. Int. Stat. Rev. 69, 21–26 (2001)

    Article  Google Scholar 

  2. Breimann, L.: Statistical modelling: the two cultures. Stat. Sci. 16, 199–231 (2001)

    Article  Google Scholar 

  3. Donoho, D.: 50 years of Data Science. J. Comput. Graph. Stat. 26(4), 745–766 (2017)

    Article  MathSciNet  Google Scholar 

  4. Molenberghs, G., Fitzmaurice, G., Kenward, M.G., Tsiatis, A., Verbeke, G.: Handbook of Missing Data Methology. CRC Press, Boca Raton (2015)

    MATH  Google Scholar 

  5. Wooldridge, J.M.: Introductory Econometrics: A Modern Approach, 5th edn. South-Western, Mason (2013)

    Google Scholar 

  6. Carroll, R., Ruppert, D., Stefanski, L.A., Crainiceanu, C.M.: Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edn. CRC Press, Boca Raton (2006)

    Book  Google Scholar 

Download references

Funding

Funding was provided by Elitenetzwerk Bayern (Grant No. S-NW-2015-313, Elitestudiengang Data Science).

Author information

Authors and Affiliations

  1. Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, 80539, Munich, Germany

    Göran Kauermann

  2. Institut für Informatik, Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538, Munich, Germany

    Thomas Seidl

Authors
  1. Göran Kauermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Seidl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Göran Kauermann.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kauermann, G., Seidl, T. Data Science: a proposal for a curriculum. Int J Data Sci Anal 6, 195–199 (2018). https://doi.org/10.1007/s41060-018-0113-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-018-0113-2

Keywords

Search

Navigation