Skip to main content
Till KTH:s startsida Till KTH:s startsida

DD2430 Project Course in Data Science 7.5 credits

Administer About course

Choose semester and 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.

Application

For course offering

Autumn 2024 DSPHT23 programme students

Application code

50273

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. 

Equipment

No information inserted

Literature

No information inserted

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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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

Add-on studies

No information inserted

Contact

Danica Kragic Jensfelt (dani@kth.se)

Supplementary information

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