Problems and Promises of Big Data Analyses


Automated strategies for knowledge production are entering many scientific fields, and algorithms are increasingly guiding decision-making on economic, judicial and medical issues. But how reliable are the results of big data analyses? And what ethical and societal issues do the use of such analyses create?

The course will give students a basic understanding of (machine learning) techniques used in big data analyses and their implications for science and society. We introduce the regulatory frameworks governing data use and provide students with tools and concepts needed for a systematic analysis of issues related to the use of big data in science and society. 

Applying a theoretical background from philosophy of science and ethics, we will analyze a number of concrete cases, such as the use of self-monitoring data, to illustrate issues related to big data. Cases will as far as possible be chosen depending on the participants’ educational background. 

Examples of topics include:

  • Choices and challenges of collecting and classifying data
  • Risk and uncertainty 
  • Privacy and the use of personal data: what is personal data and how should I handle it?
  • Justice, bias and discrimination 
  • Accountability and expertise 
  • Societal impact of big data analyses


The course will be taught as a combination of lectures, class discussions and individual project work, where students are allowed to give in-depth analysis of a case of their own choosing under supervision.

Engelsk titel

Problems and Promises of Big Data Analyses


After following the course students should have the following skills, knowledge and competences:

Knowledge about

  • Regulatory frameworks governing data use
  • Basic procedures for selected big data methods
  • Epistemic and ethical issues raised by the use of big data
  • Central concepts in big data ethics and epistemology


Skills to

  • Identify regulatory, ethical and societal issues in cases of big data analyses
  • Identify potential scientific uncertainty in cases of big data analyses
  • Analyze cases of big data analyses using regulatory, ethical and epistemological concepts



  • Discuss and critically reflect on regulatory, ethical and societal issues in cases of big data analyses in various domains 
  • Discuss and critically reflect on uncertainty in cases of big data analyses in various domains
  • Discuss and critically reflect on the relation between regulatory, ethical, societal and issues of scientific uncertainty

Lectures, group work, project seminars.

Students will be given a collection of research papers and excerpts from textbooks. 

Peerfeedback (studerende giver hinanden feedback)
7,5 ECTS
Mundtlig prøve, 20 min med opsyn.
A 20 minute oral exam based on individual written project.
Krav til indstilling til eksamen

An individual project must be handed in and passed.

Kun visse hjælpemidler tilladt

The student can bring the project report to the exam, no other aids are allowed.

7-trins skala
Ingen ekstern censur
Internal co-examiners.

The exam week for block 2.


Same as ordinary. If the exam registration requirements were not met an individual project must be approved. The project must be handed in at least three weeks before the reexam week. 

Kriterier for bedømmelse

See learning goals.

Enkeltfag dagtimer (tompladsordning)

  • Kategori
  • Timer
  • Forelæsninger
  • 24
  • Teoretiske øvelser
  • 24
  • Vejledning
  • 2
  • Eksamensforberedelse
  • 155
  • Eksamen
  • 1
  • Total
  • 206


7,5 ECTS

1 blok

Blok 2
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studienævn for Geovidenskaber og Planlægning
Udbydende institut
  • Institut for Naturfagenes Didaktik
Udbydende fakultet
  • Det Natur- og Biovidenskabelige Fakultet
  • Mikkel Willum Johansen   (3-737d70466f746a34717b346a71)

Mikkel Willum Johansen, Sune Hannibal Holm, Sara Green.

Gemt den 19-02-2024

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students