Official course description:

Basic info last published 7/11-18
Course info
ECTS points:
Course code:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
BSc in Data Science
Course semester
Forår 2019
28 January 2019
31 May 2019

This course combines knowledge from the three first-semester data science undergraduate courses (1) Introduction to Data Science and Programming, (2) Applied Statistics, (3) Data science in Research, Business and Society with knowledge that will be acquired during the second semester from the two concurrent courses.


The course will give students the opportunity to work as a team and combine their existing knowledge with the topics covered in class, in constructing and reflecting on solutions for problems over real-world data.  There is extensive cooperation with industry.

The course consists of a series of full-fledged Data Science mini-projects from start to finish, including the initial memo, technical translation of the problem, some methodology decisions, implementation, evaluation, and translation of the results back into non-technical language.  For each mini-project, there is one week of support lectures to supplement competencies required for completing the mini-project.

Formal prerequisites
(1) Introduction to Data Science and Programming, (2) Applied Statistics, (3) Data science in Research, Business and Society. Information about the course of study. This course is mandatory for students who are enrolled on BSc in Data Science and part of the second semester. The course is only open for students enrolled in BSc in Data Science.
Intended learning outcomes

After the course, the student should be able to:

  • Organize, plan, and carry out collaborative work in a smaller project group.
  • Identify, define and delimit a problem in Data Science (i.e., prepare a problem statement)
  • Quickly preprocess a wide range of raw data
  • Translate diverse problem settings into individual well-defined data analysis problems
  • Identify and analyse relevant options for an appropriate basic methodology for the problem (data structures, algorithms, statistical methods), including those lectured on specifically for the mini-project
  • Compare the relevant options for the task, selecting the most suitable ones, both practically and theoretically
  • Implement the methodology and carry out the analysis
  • Document the project incrementally through the project diary and detailed control log
  • Translate the findings back to the problem domain
  • Carry out extensive error analysis and reflect on the method and results
  • Provide a succinct oral and written explanation of the problems for each mini-project to both experts and non-experts, including a short description, the method, and the outcomes