PDF for print Find calendar

Specialization Course in Computer Science - Data Science

Title
Specialization Course in Computer Science - Data Science
Semester
F2023
Master programme in
Computer Science
Type of activity

Course
Teaching language
English
Study regulation

Read about the Master Programme and find the Study Regulations at ruc.dk
REGISTRATION AND STUDY ADMINISTRATIVE
Registration

Sign up for study activities at STADS Online Student Service within the announced registration period, as you can see on the Study administration homepage. When signing up for study activities, please be aware of potential conflicts between study activities or exam dates. The planning of activities at Roskilde University is based on the recommended study programs which do not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programs, an overlap of lectures or exam dates may occur depending on which courses you choose.
Number of participants
ECTS
5
Responsible for the activity
Henning Christiansen (henning@ruc.dk)
Jens Classen (classen@ruc.dk)
Head of study
Henrik Bulskov (bulskov@ruc.dk)
Teachers
Study administration
IMT Registration & Exams (imt-exams@ruc.dk)
Exam code(s)
U60479
ACADEMIC CONTENT
Overall objective

Specialization within one of the core specialization areas of the program. The student must acquire knowledge, skills and competences in order to translate theories, methods and solutions ideas into their own practice in relation to software development.

1) Specialization course with a focus area towards algorithms, programming frameworks and complex IT systems. 2) Specialization course with a focus area towards data science, artificial intelligence and business intelligence. 3) Specialization course with a focus area within e.g. internet of things, robotics and virtual technologies.
Detailed description of content

In this specialization, we will approach data science in a systematic way and focus on key aspects such as gradient decent learning based on lost functions, the bias-variance (or underfitting/overfitting) trade-off, model selection, model evaluation, explainability, sampling techniques, reinforcement learning, Bayesian approaches, Deep Learning, etc.
Course material and Reading list

Will be announced on Moodle.
Overall plan and expected work effort
Format
Evaluation and feedback
Programme
ASSESSMENT
Overall learning outcomes

After completing this course, students will be able to:

  • demonstrate knowledge and understanding of one or more of the specialization areas and the area’s techniques for designing and constructing complex software systems.

  • know and understand the general principles behind the specialization area’s theory, methods, and technological solutions.

  • elect and apply appropriate methods and techniques from the specialization area to analyse, design and construct reliable and user-friendly systems.

  • become proficient in new approaches to the specialization area.
Form of examination
Individual oral exam based on a written product..

The character limit of the written product is maximum 48.000 characters, including spaces.
The character limits include the cover, table of contents, bibliography, figures and other illustrations, but exclude any appendices.

Time allowed for exam including time used for assessment: 20 minutes.
The assessment is an overall assessment of the written product(s) and the subsequent oral examination.

Permitted support and preparation materials for the oral exam: All.

Assessment: 7-point grading scale.
Moderation: Internal co-assessor.
Form of Re-examination
Samme som ordinær eksamen / same form as ordinary exam
Type of examination in special cases
Examination and assessment criteria

The assessment will be based on the extent to which the student:

  • Demonstrates familiarity with the selection of tools for and approaches to Data Science applied in the course.
  • Demonstrates knowledge of current research trends in Data Science.
  • Demonstrates understanding of the process of finding and adapting data sets, selecting relevant hypotheses and applying suitable Data Science methods.
Exam code(s)
Exam code(s) : U60479
Last changed 23/03/2023

lecture list:

Show lessons for Subclass: 1 Find calendar (1) PDF for print (1)

Monday 06-03-2023 08:15 - 06-03-2023 12:00 in week 10
Data Science (COMP)

Wednesday 08-03-2023 08:15 - 08-03-2023 16:00 in week 10
Data Science (COMP)

Friday 10-03-2023 08:15 - 10-03-2023 12:00 in week 10
Data Science (COMP)

Monday 13-03-2023 08:15 - 13-03-2023 12:00 in week 11
Data Science (COMP)

Wednesday 15-03-2023 08:15 - 15-03-2023 16:00 in week 11
Data Science (COMP)

Friday 17-03-2023 08:15 - 17-03-2023 12:00 in week 11
Data Science (COMP)

Monday 20-03-2023 08:15 - 20-03-2023 12:00 in week 12
Data Science (COMP)

Sunday 26-03-2023 20:00 - 26-03-2023 20:00 in week 12
Data Science - Hand-in (COMP)

Friday 31-03-2023 08:15 - 31-03-2023 18:00 in week 13
Data Science - Oral examination (COMP)

Monday 14-08-2023 10:00 - 14-08-2023 10:00 in week 33
Data Science - Reexam - Hand-in (COMP)

Friday 18-08-2023 08:15 - 18-08-2023 18:00 in week 33
Data Science - Oral reexamination (COMP)