DD2430 Project Course in Data Science 7.5 credits

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.

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.

DD2421 Machine learning or the equivalent.

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. 

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

• 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.

Danica Kragic Jensfelt

• 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 about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

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