DA2210 Introduction to the Philosophy of Science and Research Methodology for Computer Scientists 6.0 credits

Vetenskapsteori och vetenskaplig metodik för dataloger

Please note

The information on this page is based on a course syllabus that is not yet valid.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Autumn 19 vettig19 for programme students

Autumn 18 vettig18 for programme students

Intended learning outcomes

Having passed the course, the student should be able to:

  • explain and analyse scientific theories relevant for research in computer science,
  • explain and analyse scientific methods relevant for research in computer science,
  • review scientific articles in computer science with regard to theory, method and result critically
  • identify methodological problems in a study
  • identify ethical problems in different scientific situations and discuss them
  • plan and carry out the writing of a scientific report.

Course main content

  • The basic concepts within philosophy of science and research methodology, such as causality, data, correlation, hypothesis, inductive-deductive methods.
  • Special methods and problems within computer science and mathematics.
  • Research methodology within engineering projects.
  • Experimental methodology.
  • Ethics in science and the role of science in society.
  • How to read and write scientific reports.
  • Practical training in writing of scientific reports (similar to degree projects).


Lectures that cover the main theoretical results and basic scientific methods.

Seminars, in which the students, in groups and individually, are trained in reading, describing and evaluating scientific experiments and reports.

Practical training to write shorter and longer scientific reports that apply the methods and theories that have been gone through during the course.


Recommended prerequisites

Corresponding the qualification requirements for Master of Science in Computer Science or Machine Learning.


Kurslitteratur meddelas senast 4 veckor innan kursstart på kursens hemsida.

Required equipment


  • HEM1 - Exercises, 1.5, grading scale: P, F
  • HEM3 - Essay, 1.5, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Examination, 3.0, grading scale: A, B, C, D, E, FX, F

Requirements for final grade

Exam and home assignments.

Offered by

EECS/Computer Science


Linda Kann, e-post: lk@kth.se


Johan Karlander <karlan@kth.se>

Add-on studies

Discuss with the instructor.


Course syllabus valid from: Autumn 2019.
Examination information valid from: Spring 2019.