053631 LP Data Analysis Project (2023W)

12.00 ECTS (8.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Registration is open from We 13.09.2023 09:00 to We 20.09.2023 09:00
Deregistration possible until Sa 14.10.2023 23:59

Details

max. 25 participants

Language: English

Lecturers

Classes

Registration process:

1. Look through the list of projects, finding projects of interest.
2. Send an email to the project supervisor, providing the following information (an example email is in the Guideline https://moodle.univie.ac.at/mod/resource/view.php?id=17824160): name, study program, pre-requisites, and a sentence or two expressing why the project seems interesting. Attach your transcript. Keep it professional and brief.
3. Wait for supervisor to agree to supervise you on the project. Only once they agree, submit the form on Moodle (https://moodle.univie.ac.at/mod/questionnaire/view.php?id=17824180). The deadline for this submission is 20. September if you want to ensure participation in the class.
4. If you have not had a supervisor agree to supervise you by 11. September, please contact Drew Dimmery (drew.dimmery@univie.ac.at) who will help find you a project.
5. Register for the DA Projects course (number 053631) during the normal registration period from 13. September to 20. September. You will be placed on a waiting list until we confirm that a supervisor has agreed to supervise you.

Information

Aims, contents and method of the course

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" in June 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.

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

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

Examination topics

To be determined by project supervisors.

Reading list