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Project Course in Data Science

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Course contents 

The course will start with a part, where we study how a scientific article is constructed. The students will then, in groups of 2-5, choose articles in their sub-track (machine learning, natural language processing or bioinformatics), implement the method in the article and recreate the experiment. The type of project therefore will vary depending on sub-track, but the intended learning outcomes are the same for all three sub-tracks. The aim of the course is to bridge the gap between the courses in each sub-track and the degree project.

Intended learning outcomes 

After passing the course, the students should be able to:

  • read scientific articles critically
  • reproduce methods in articles
  • plan and carry out work in a group.

Course Disposition

Reading, implementation, evaluation and then written report.

Literature and preparations

Specific prerequisites 

DD2421 Machine learning or the equivalent.

Recommended prerequisites

The student should have completed most of the courses in one of the subtracks of the track Data Science in the Computer Science masters programme. 


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A number of research articles that are chosen based on the theme.

Examination and completion

Grading scale *

P, F

Examination *

  • PRO1 - Project report, 3.5 credits, Grading scale: P, F
  • PRO2 - Oral evaluation, 4.0 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

In this course, the code of honor of the school is applied, see: http://www.kth.se/en/csc/utbildning/hederskodex

The examiner will decide on possible adapted examination for students with documented, permanent disabilities. The examiner may admit other examination format for re-examination of individual students.