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.
DD2430 Project Course in Data Science 7.5 credits
![](https://kursinfostorageprod.blob.core.windows.net/kursinfo-image-container/Picture_by_MainFieldOfStudy_03_Computer_Science.jpg)
About course offering
For course offering
Autumn 2024 DSPHT23 programme students
Target group
No information insertedPart of programme
Master's Programme, Computer Science, åk 2, CSDA, Mandatory
Master's Programme, Machine Learning, åk 2, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 1, Recommended
Periods
P1 (3.5 hp), P2 (4.0 hp)Duration
Pace of study
25%
Form of study
Normal Daytime
Language of instruction
English
Course location
KTH Campus
Number of places
Places are not limited
Planned modular schedule
Course memo
Course memo is not publishedSchedule
Link to scheduleApplication
For course offering
Autumn 2024 DSPHT23 programme students
Application code
50273
Contact
For course offering
Autumn 2024 DSPHT23 programme students
Contact
Danica Kragic Jensfelt (dani@kth.se)
Examiner
No information insertedCourse coordinator
No information insertedTeachers
No information insertedContent and learning outcomes
Course contents
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
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
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
Opportunity to raise an approved grade via renewed examination
Examiner
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
Offered by
Main field of study
Education cycle
Add-on studies
Contact
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex