Seminars in Data Science (Autumn 2022)

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.

reexam

Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1H: Oral exam with time for preparation. Home.

Time and date