NNDK19001U  Problems and Promises of Big Data Science

Volume 2019/2020
Content

Big data is everywhere! 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 analysis? Can an algorithm be sexist? And what about privacy? 

 

The course will give students a basic understanding of techniques used in big data analysis 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 illustrating issues related to big data. Examples of topics are risk and uncertainty, privacy, justice and discrimination, and accountability and expertise. 

 

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.

Learning Outcome

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 and ethical issues in cases of big data analysis

-      Identify potential scientific uncertainty in cases of big data analysis

-      Analyze cases of big data analysis using regulatory, ethical and epistemological concepts

 

Competences

-      Discuss and critically reflect on regulatory and ethical issues in cases of big data analyses in various domains 

-      Discuss and critically reflect on uncertainty in cases of big data analysis in various domains

-      Discuss and critically reflect on the relation between regulatory, ethical and epistemological issues of scientific uncertainty

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

Academic qualifications equivalent to a BSc degree is recommended.
Lectures, group work, project seminars.
Written
Oral
Peer feedback (Students give each other feedback)
Credit
7,5 ECTS
Type of assessment
Oral examination
Oral exam 20 minutes based on individual written project
Exam registration requirements

An individual project must be handed in and passed.

Aid
Only certain aids allowed

The project report from the individual written project is the only aid allowed.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as ordinary. If the exam registration requirements were not met, an individual project must be handed in and approved. The project must be handed in at least 3 weeks before the reexam week. 

Criteria for exam assesment

See Learning Outcome 

 

  • Category
  • Hours
  • Lectures
  • 24
  • Theory exercises
  • 24
  • Guidance
  • 2
  • Preparation
  • 155
  • Exam
  • 1
  • Total
  • 206