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DD2430 Project Course in Data Science 7.5 credits

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Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Autumn 2025 Start 25 Aug 2025 programme students

Course location

KTH Campus

Duration
25 Aug 2025 - 12 Jan 2026
Periods
P1 (3.5 hp), P2 (4.0 hp)
Pace of study

25%

Application code

50912

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
[object Object]

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD2430 (Autumn 2021–)
Headings with content from the Course syllabus DD2430 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

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.

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. 

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

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.

Examiner

Profile picture Danica Kragic Jensfelt

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Intelligent Systems

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex