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

Vetenskapsteori och vetenskaplig metodik för dataloger

  • 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

On completion of the course, the student should be able to

  • account too and analyse scientific theories relevant to research within computer science
  • account too and analyse scientific methods relevant to research within computer science
  • review scientific articles in computer science regarding theory, method and results critically
  • identify methodological problems in a study
  • identify ethical problems in different scientific situations and be able to reason about them
  • be able to plan and the genomföraskrivandet of a scientific report

Course main content

  • The basic concepts within theory of knowledge and scientific methodology, so as causality, data, correlation, hypothesis, induktiva-deduktiva methods.
  • Special methods and problem within computer science and mathematics.
  • Scientific methodology within ingenjörsprojekt.
  • Experimentmetodik.
  • Ethics within scholarship and the vetenskaparens role in the society.
  • How one reads and writes scientific reports.
  • Practical training in the writing of scientific reports (similar degree projects).

Introduction to history of science. Presentation of the most important the issues within
theory of knowledge. Methods and concept within natural sciences: Causality, data, correlation, hypotheses and hjälphypoteser, hypothetical - deductive method. Scientific methods within mathematics.
Scientific methods within computer science. Demarkation between scholarship and pseudoscience.
Study of scientific articles. Ethics with a specialisation in scientific ethics. Case studies of
ethical problems. Orientation in the social role of researcher.

The course intends to give a broad introduction to theory of knowledge especially with specialisation in
mathematics, natural sciences and computer science. A short overview of history of science is given. The most important
the thoughts within general theory of knowledge are presented and analysed. Poppers and Kuhn scientific philosophy are presented.
The most important general scientific methods within mathematics, natural sciences and computer science
be presented and analysed. Concept as causality, realism and antirealism, hypotheses and hjälphypoteser be discussed. A review of the ethical problems of the scholarship and the researcher's role is made. Practically work with to write scientific reports (similar degree projects).


Lectures that treat theoretical the huvudresultat and basic scientific methods.

Seminars, in which the students joint and be trained individually in to read about, describe and evaluate scientific experiments and reports.

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


  • 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

Examination 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: Spring 2019.
Examination information valid from: Spring 2019.