Universität Wien
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300065 VU Quantitative analysis of time series and population data (2024W)

(Data Analysis and modeling)

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Prüfungsimmanente Lehrveranstaltung
Moodle
Mi 22.01. 13:15-16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 03.10. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 10.10. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 17.10. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 24.10. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 31.10. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 07.11. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 13.11. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 14.11. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 20.11. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 21.11. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 27.11. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 28.11. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 04.12. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 05.12. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 11.12. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 12.12. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 08.01. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 09.01. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 15.01. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 16.01. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Donnerstag 23.01. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
  • Mittwoch 29.01. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Donnerstag 30.01. 09:45 - 11:15 Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Learning objectives

The students will be able to conduct a basic quantitative and statistical analysis of time series data and data from population samples.
The students have a general understanding of time series data and will be able to perform different interpolation, regression, and fitting techniques on it. They know statistical tools of time series analysis and general time series modelling approaches, and are able to apply them to a given dataset.
The students understand the basic properties of population sampled data. They are able to perform a density estimation on that data, and fit the result to either a single parametrized distribution or a mixture of multiple distributions.
Students are able to perform the discussed analysis techniques in Python, and can interpret their analysis results from time series or population data in a biological context.

Contents

Time series analysis: basic properties, analysis techniques, statistics, models
Population data analysis: density estimation, distribution fitting, mixture modelling
Basic statistical tools: covariance analysis, maximum likelihood estimation, confidence intervals

Methods

Lecture, computer exercise, activities on the online learning platform

Art der Leistungskontrolle und erlaubte Hilfsmittel

- Written exam (60%)
- Solutions of computer exercises (20%)
- Student activity (20%)

Mindestanforderungen und Beurteilungsmaßstab

All partial evaluations must be positive to pass the course. Minimum score to pass is 50% in total.

Prüfungsstoff

In the exam, students need to show that they are able to apply the methods discussed in the course to biological data analysis problems.

Literatur

Literature references are provided in the lecture handouts on Moodle.

Zuordnung im Vorlesungsverzeichnis

MEC-5, MEC-9, MNB2, MBO 7, MZO3, MES4

Letzte Änderung: Mo 30.09.2024 09:26