Allgemeine Informationen

Untertitel
Veranstaltungsnummer	5943UE
Semester	WiSe 24/25
Aktuelle Anzahl der Teilnehmenden	68
erwartete Teilnehmendenanzahl	20
Heimat-Einrichtung	Lehrstuhl für Data Science
beteiligte Einrichtungen	Lehrstuhl für Informatik mit Schwerpunkt Verteilte Informationssysteme
Veranstaltungstyp	Übung in der Kategorie Lehre (mit Prüfung)
Nächster Termin	Mi., 30.10.2024 14:00 - 18:00 Uhr, Ort: (ITZ) SR 011
Art/Form
Teilnehmende	1.-3. Semester Master
Voraussetzungen	Visual Analytics or Network Science or Advanced Topics in Data Science IMPORTANT Participation has to be confirmed via signature in the first lecture. Students that do not attend the first lecture will be signed out of the course, giving a chance to the students from the waiting list to be signed in. If a student drops out of the course, a negative mark will be given (there is no dropping out), so assess your availability for doing this course, in relation to other obligations you have.
Lernorganisation	Kenntnisse / Skills/Knowledge: Students will acquire knowledge of current data analysis technologies and corresponding python libraries to analyze web-based data sets such as Web pages, social networks, user data, etc. They will obtain methodological knowledge Fähigkeiten / Abilities: Students acquire the ability to apply data science technology on web data and to extract interesting patterns from very large data sets. They will develop the ability to use appropriate software libraries and tools to do so. Kompetenzen / Competencies: Students acquire the skills to analyze massive, web-based data sets and extract interesting patterns.
Leistungsnachweis	Portfolio exam consisting of a written technical report on the outcome of the project and 4 presentations (one per phase / per team member).
SWS	4
Literatur	Own Lecture Notes and selected publications. Literature will be announced depending on the concrete topics.
Sonstiges	Zuordnung zum Curriculum / Curriculum PO 2013: Wahlpflichtmodul im Schwerpunkt Informations- und Kommunikationssysteme / compulsory elective module with a focus on information and communication systems Wahlmodul im Schwerpunkt Algorithmik und Mathematische Modellierung / elective module with a focus on Algorithms and Mathematical Modelling PO 2016: Modulgruppe „Informations- und Kommunikationssysteme“ / focus “information and communication systems”
ECTS-Punkte	6

Lehrende

Veranstaltungsort / Veranstaltungszeiten

(ITZ) SR 011

Mi. 14:00 - 18:00 (15x)

Studienbereiche

Die Angaben zu den Anrechenbarkeiten an der FIM sind ohne Gewähr. Bitte beachten Sie die verbindliche Liste der Anrechenbarkeiten .

Universität Passau
- Studienangebote in anderen Sprachen
  - Studienangebot in englischer Sprache
- Gefährdungspotenzial
  - Kein Gefährdungspotential der Veranstaltung für werdende und stillende Mütter (grün)
- Fakultät für Informatik und Mathematik
  - Studiengänge
    - Master Artificial Intelligence Engineering
    - Master Computational Mathematics
    - Master Informatik

Modulzuordnungen

Kommentar/Beschreibung

IMPORTANT:

This course needs programming skills and data understanding, please do not register if you are not familiar with Python.
The student selection is due to end on Oct 20.

Content:
Students will work in groups on selected data science specific problems, like for example extracting communities from social networks, clustering web pages, analyzing trends in social media or identifying mobility patterns.
Students will be given a small research projects in the form of an analysis goal, a data set and a target metric. The research project will be conducted in four phases, supervised by the lecture. In every phase, one team member takes the responsibility. The following phases are foreseen:
1. Design Phase:
Students will conduct a state of the art analysis on currently best performing methods on the domain and corresponding libraries. Based on this analysis, students will design their experiment in terms of analysis methods, data preprocessing and evaluation approach. The experimental design will be reported in the form of a presentation.
2. Data Preprocessing:
Students will apply data preprocessing methods in order to convert raw data into a usable format for subsequent data analysis. Results are reported in the form of a presentation.
3. Data Analysis:
Students will implement the chosen data analysis methods using selected libraries and apply the implementation to the preprocessed data. Results are reported in the form of a presentation.
4. Evaluation:
Students will evaluate different parameter settings and algorithmic combinations or derive patterns from the given data set and interpret those.
Finally, the results will be reported in a technical report.

Anmeldemodus

Die Auswahl der Teilnehmenden wird nach der Eintragung manuell vorgenommen.

Nutzer/-innen, die sich für diese Veranstaltung eintragen möchten, erhalten nähere Hinweise und können sich dann noch gegen eine Teilnahme entscheiden.