BSc Vertiefung and MSc Basismodule

B.Sc. Vertiefung

The B.Sc. Vertiefung is a major element of our BSc curriculum.  In order to apply for a Vertiefung you need to make an appointment by contacting ddis-teaching@ifi.uzh.ch. At the appointment we will discuss the possible options and agree on a subject. I usually, try to customize the subject to your interest. Below you will find a list of typically chosen subjects (they are very similar to the MSc Basismodul subjects, but, ususally, with less depth).

In conjunction with whatever reading we agree upon you are usually asked to do some assignment, a little programming task, or an analysis to apply the knowledge you learned.

M.Sc. Basismodul

The M.Sc. Basismodul is a major element of our M.Sc. curriculum and must be completed with an oral exam before the start of both the lecture period of the 3rd semester and the Master’s project. In order to apply for a M.Sc. Basismodul you need to make an appointment.

At the appointment your study plans will be discussed. So you need to bring a plan of classes you intend to take during your M.Sc. studies with you. This appointment is intended to help you organize your coursework and your goals. Then, we look at possible options for the Basismodul and agree on a subject.  We try to customize the subject to your interest, so let us know your interests beforehand.

To enroll for a M.Sc. Basismodul you need to make an appointment by contacting ddis-teaching@ifi.uzh.ch and include a selection of one or more of the topics listed below.

Typical subject areas for B.Sc. Vertiefung or Basismdul

  • Data privacy
    • Differential privacy
    • Privacy methods for data publication
  • Multimedia
    • Retrieval
    • Management
    • Processing
    • Analysis
  • Semantic Web
    • Data Integration
    • Data Quality Assurance
    • Graph embeddings
    • Knowledge Engineering
    • Knowledge Graphs (Wikidata, DBPedia, Freebase)
    • Knowledge Representation and Logics
    • Querying and Reasoning
    • Stream Reasoning
    • Triple Stores
  • Social Computing
    • Collaborative Data Science
    • Computer-Supported Cooperative Work
    • Human-Machine Integration, Crowdsourced Human Computation
  • Stream Processing
    • Big Data Stream Processing Frameworks
    • Mining dynamic data (e.g. time series and data streams)
    • Scheduling
    • Query languages