DE / EN

Seminar Data-Science II (Empirical Studies)

IS 723 for Master students (M.Sc. MMM, M.Sc. WiPäd, MMDS)

LecturerProf. Dr. Markus Strohmaier, Dr. Max Pellert, Tobias Schumacher
Course FormatSeminar
OfferingFSS
Credit Points6 ECTS
LanguageEnglish
GradingWritten report (40%), oral presentations (60%)
Examination dateSee schedule below
Information for Students

The course is limited to 12 participants. The registration process is explained below.

Tobias Schumacher, M.Sc.

Tobias Schumacher, M.Sc.

For further information please contact Tobias Schumacher.

Max Pellert

Max Pellert

For further information please contact Max Pellert.

Course Information

  • Course Description

    The learning goals will be achieved by in-depth learning from examples on the basis of personally assigned scientific topics as well as by active participation in the presentation sessions. The organizer will choose subject areas within the field of data science and provide scientific papers for the students to work through.

    Previous participation in courses offered by our chair is recommended.

  • Objectives

    On the basis of scientific literature, in particular original research articles, students independently familiarize themselves with one topic in data science. They classify and narrow down the scope of the topic appropriately and provide a critical evaluation. Students work out concepts, procedures and results of a given topic clearly and with appropriate formalisms. They do so in-depth by writing in a concise manner and to a defined extent. They are expected to show evidence of independent development of their arguments by presenting self-selected examples. Towards the end of the course they give a descriptive, in-depth oral presentation of the data science topic using suitable media and examples in an agreed-on format.

  • Topics

    Social media inevitably have a strong impact on societies in a globalized world. At their best, they provide chances for people to form communities which maybe could not have developed offline. At the same time, the algorithms powering these platforms can also create problems and harm users. The platforms' public nature allows us to gain more understanding of these issues and about humans in general, as there has never been as much data provided by people about what they think or feel.

    In this seminar, we will discuss recent research studies that employ state-of-the-art data mining and knowledge discovery techniques to empirically analyze two aspects of social media, namely the dynamics in online communities and digital wellbeing.

    When applying for this seminar, please indicate which of these two broad topics you are more interested in. The exact research articles that are to be worked on will be determined once we have an overview over the number of participants and their rough preferences.

  • Schedule

    Registration period

    01.01.23 – 13.02.23 (11.59 PM) see „Registration“
    Notification of acceptance/rejection15.02.23 (lunchtime) 
    Kick-off meeting

    20.02.23 (12:00 – 13:30)
    @ B 112 Laborraum (L 15, 14–17 (Anbau))   

    assignment of seminar topics
    Drop-out until16.02.23 (lunchtime) 
    Midterm

    31.03.23 (09:00 – 17:00, longer/shorter depending on the number of participants)

    midterm presentations
    Endterm

    26.05.23 (09:00 – 17:00, longer/shorter depending on the number of participants)

    endterm presentations
    Submission deadlineto be announced 
  • Registration

    If you are interested in this seminar, please apply to Tobias Schumacher and Max Pellert via email. 

    Please start the Subject Line with “[SemDSII]”, and provide some details about your background, e.g., whether you have taken some relevant classes before (cf. course description) and your motivation to take this seminar. Also, make sure to indicate which of the topics outlined above you are most interested in.