Vorlesungs­verzeichnis

Data Analysis Project

053631 LP 2023S

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Vortragende der Germanistik:

• Ludwig M. Breuer
• Claudia Resch

To register for the Projects course, you will need to follow a slightly different procedure than for a typical course:

1. Send a completed application form (https://moodle.univie.ac.at/mod/resource/view.php?id=13939843 ) to info.datascience@univie.ac.at. Please use the subject line "S2023 Application - <Your name>". These preferences for projects should be sent in by 24. February to guarantee time for matching of students to projects. I will send a reminder.


2. During the normal course registration period, register for the course. You will be placed on a waiting list pending a match to a project.


3. You will be contacted by your project supervisor at the start of term and admitted formally to the course.

Filling in the application form includes:

• A list of three projects (ranked in terms of interest)


• If you have been pre-allocated to a project due to explicit discussion with a supervisor, you only need to list that one project (and note that you discussed this with the supervisor).


• For each project, provide a list of the project-specific pre-requirements and confirmation that they have been satisfied. If the requirement specifies a concept instead of a course, please provide the course in which you learned about the concept (if you did).


• If there's any additional information we should have when matching you to projects, please include that (but briefly: no more than 3 sentences). For example, perhaps you already know that you really want to work in a particular field (and a particular project is highly relevant to that).


• Attach a copy of a transcript demonstrating that project-specific pre-requirements have been satisfied.

 

Ziele, Inhalte und Methode der Lehrveranstaltung

In the course of a data analysis project, students acquire the ability to solve data science projects using the methods and techniques that the students have already learned during their studies. The range of possible project topics is quite broad, ranging from theoretical questions to applied topics with a potential industry partnership. Each project should be targeted at groups of 1-4 students, who will work on the project for the full semester, in addition to taking other classes. Each project will be supervised by our teaching staff, sometimes in cooperation with an industry partner. Common sessions and meetings will be arranged and agreed upon with the respective supervisor/s.

We are planning a joint "Data Science Day" at the beginning of summer semester 2024, in which the students present their work in a poster session to a broader audience including first and second semester students of the Data Science programs.

 

Art der Leistungskontrolle und erlaubte Hilfsmittel

The project must be completed by the end of the term.

Each project will consist of an implementation part (25%), documentation part (25%), presentation/poster part (25%), and participation part (25%) – to be specified by the particular project supervisor/s.

 

Literatur

To be determined by project supervisors.

 

Prüfungsstoff

To be determined by project supervisors.

 

Mindestanforderungen und Beurteilungsmaßstab

The project must be completed by the end of the term. To pass, the average grade based on above examinations must be at least sufficient / 4.0.