DD2430 Project Course in Data Science 7.5 credits

Projektkurs i dataanalys

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

    P, F

Course offerings

Autumn 19 for programme students

Autumn 18 DSProjHT18 for programme students FULL

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 main content

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.

Disposition

Reading, implementation, evaluation and then written report.

Eligibility

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

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

Examination

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

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.

Offered by

EECS/Intelligent Systems

Contact

Danica Kragic Jensfelt (dani@kth.se)

Examiner

Danica Kragic Jensfelt <dani@kth.se>

Version

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