300065 VU Quantitative analysis of time series and population data (2023W)
(Data Analysis and modeling)
Continuous assessment of course work
Labels
ON-SITE
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 Th 07.09.2023 14:00 to Th 21.09.2023 18:00
- Deregistration possible until Su 15.10.2023 18:00
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
05.10.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Thursday
12.10.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Thursday
19.10.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Thursday
09.11.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Thursday
16.11.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Wednesday
22.11.
13:15 - 16:30
Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Thursday
23.11.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Wednesday
29.11.
13:15 - 16:30
Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Thursday
30.11.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Wednesday
06.12.
13:15 - 16:30
Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Thursday
07.12.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Wednesday
10.01.
13:15 - 16:30
Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Thursday
11.01.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Wednesday
17.01.
13:15 - 16:30
Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Thursday
18.01.
09:45 - 11:15
Seminarraum 1.7, Biologie Djerassiplatz 1, 1.010, Ebene 1
Thursday
25.01.
09:45 - 11:15
Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
Information
Aims, contents and method of the course
Assessment and permitted materials
- Written exam (60%)
- Solutions of computer exercises (20%)
- Student activity (20%)
- 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, MES4
Last modified: Th 11.01.2024 11:46
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.ContentsTime 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 intervalsMethodsLecture, computer exercise, activities on the online learning platform