Universität Wien
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300065 VU Quantitative analysis of time series and population data (2022W)

(Data Analysis and modeling)

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
Moodle

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).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 06.10. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 13.10. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 27.10. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Wednesday 09.11. 13:15 - 16:30 Seminarraum 1.2, Biologie Djerassiplatz 1, 1.004, Ebene 1
  • Wednesday 09.11. 15:00 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Thursday 10.11. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 17.11. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 01.12. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Wednesday 07.12. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Wednesday 14.12. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Wednesday 11.01. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Thursday 12.01. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Wednesday 18.01. 13:15 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Thursday 19.01. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 26.01. 09:45 - 11:15 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1

Information

Aims, contents and method of the course

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

Assessment and permitted materials

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

Minimum requirements and assessment criteria

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

Examination topics

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

Reading list

Literature references are provided in the lecture handouts on Moodle.

Association in the course directory

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

Last modified: Mo 23.01.2023 14:09