Official course description:
Full info last published 15/05-22

Seminars in Data Science

Course info
Language:
English
ECTS points:
7.5
Course code:
KSSEDAS1KU
Participants max:
47
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
Programme
Level:
MSc. Master
Programme:
MSc in Data Science
Staff
Course manager
Postdoc
Teacher
Associate Professor, Head of study programme
Teacher
Associate Professor
Teacher
Associate Professor
Course semester
Semester
Efterår 2022
Start
29 August 2022
End
31 January 2023
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

This course is a seminar-based overview of recent research on data science application areas. This course consists of a series of research-based seminars in application areas of data science. The aim is to get an overview of recent research advances in data science areas, which are possible topics for students to specialize in later parts of their MSc studies. Guest lecturers will introduce a data science topic, provide recent research samples and stipulate discussion in that area.

Description

In this seminar-based course, students will learn about the latest advances in various data science applications. The course will consist of a set of themes in Data Science each with seminars with lectures and exercise sessions, complemented by invited experts. Teachers will introduce the students to a data science research area and expose them to a latest research project

This course will cover the following main topics applied to research on Data Science:

  • Natural Language Processing
  • Computational Social Science
  • Health Informatics
  • Games Theory

This round-robin of topics will provide the students with a breadth of topics, to get them acquainted with various fields and stipulate possible projects and specializations in their later semesters. 

Formal prerequisites
Intended learning outcomes

After the course, the student should be able to:

  • Discuss, explain, and reflect upon central concepts, algorithms, and challenges in recent research for the selected Data Science areas covered in the course.
  • Engage critically with the academic literature, pointing out both strengths and weaknesses in the four research areas.
  • Distinguish and relate different theories, modeling approaches and scientific argumentations put forth in the research themes covered in this course.
  • Apply theories, computational approaches and implement methods to analyze problems in Data Science areas (e.g. Games, Health, Language and Social Science).
Learning activities

The course consists of lectures and exercises. Beyond lectures and exercise sessions, we will have various online activities to be done before and after classes.

These online activities will include: readings and videos, discussion on forums, writing documents, statistical analyses. During the class, we will have group discussions, class discussions, and quizzes.

During the classes, students will also have the opportunity to take part in exercise sessions and receive feedback.

There are no mandatory activities. The students are however strongly encouraged to participate in exercise sessions during the course to receive feedback.

Course literature

Study materials will be provided during the course from multiple sources (book excerpts, research papers, videos)

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 15%
  • Lectures: 25%
  • Exercises: 20%
  • Assignments: 10%
  • Project work, supervision included: 15%
  • Exam with preparation: 15%
Ordinary exam
Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1H: Oral exam with time for preparation. Home.
Exam duration for the preparation:
The exam consists of an oral exam with 48h of preparation at home.
A student will be assigned randomly to one of the four areas and receive a research paper, for which the student will have to prepare a 20 minutes presentation. The student will be inquired about the presentation and the four topics discussed in class.
Exam duration per student for the oral exam:
40 minutes

Time and date
Ordinary Exam Mon, 16 Jan 2023, 09:00 - 21:00
Ordinary Exam Tue, 17 Jan 2023, 09:00 - 21:00
Ordinary Exam Wed, 18 Jan 2023, 09:00 - 21:00
Ordinary Exam Thu, 19 Jan 2023, 09:00 - 21:00
Ordinary Exam Fri, 20 Jan 2023, 09:00 - 21:00
Reexam Mon, 13 Mar 2023, 12:00 - 20:00