Veranstaltung
| Titel | Advanced statistical methods for biological data analysis |
| Title | Advanced statistical methods for biological data analysis |
| Schwerpunkt/Focus | |
| Sprache/Language | englisch |
| VV-Nr./Course No. | 136028 |
| Modulverantwortlich/Responsible | Dr. P. Czuppon |
| Vertreter/Co-responsible | |
| Anbieter/Teachers | Dr. P. Czuppon |
| Typ/Type | Vorlesung, Übung / Lecture, Tutorials, Coding sessions |
| SWS/Semerster periods per week | |
| Arbeitslast(h)/Work load | 150h |
| KP/Credit points | 5 KP |
| Zuordnung/Classification | Fortgeschrittenen-Modul / Advanced Module |
| Semester/Semester | SoSe |
| Studierende/Students | MSc Biowissenschaften MSc Biotechnologie MSc Molekulare Biomedizin |
| Corona-Informationen/Corona-Information | |
| Zeit/Date | Block I: 22.4.2024-17.5.2024 |
| Ort/Location | Institute for Evolution and Biodiversity, Hüfferstrasse 1 |
| Beginn/Start | 22.4.2024 |
| Vorbesprechung/Obligatory pre-meeting | keine / none |
| Voraussetzung/Prerequisite | keine / none |
| Anmeldung/Registration | Online Anmeldung |
| Leistungskontrollen/Performance assessments | homeworks, active participation in tutorials |
| Termine f. Leistungskontrollen/Date for performance assessments | |
| max. NP/Max. grade points | 200 |
| Ziele/Aims | ability to choose an adequate statistical framework/model to analyze data; conduct a statistical analysis of data with the program R; |
| Inhalte/Content | The course is about advanced statistical concepts to analyze experimental data. The preliminary program is as follows. The first week of the course is a crash-course of basic statistical analyses and covers concepts like statistical significance, t-tests and ANOVA. The second week will address the more general framework of general linear models. In particular, this framework encompasses all of the models studied in the first week and newly introduced ones like ANCOVA. In the third week, we will talk about what to do when data is not normally distributed. The models known as generalized linear models are the flexible framework, in which one can study a large variety of experimental setups and data that is not necessarily normally distributed. In the last week, we will talk about Bayesian inference, which is a shift in viewpoint to the traditional frequentist analyses (weeks 1-3). Bayesian inference has gained a lot of popularity in the last decade, thanks to a huge increase of computational power, and is a very powerful tool to analyze highly complex data structures. Lastly, we will also talk a lot about `diagnostics' throughout the course, i.e., the assessment of the adequacy of the statistical model to describe the data. |
| Methoden/Methods | Linear regression, general linear models, generalized linear models, mixed effect/varying intercept models, Bayesian inference, model outcome diagnostics; R programming language; RStudio programming interface |
| Berufsrelevante und interdisziplinäre Komponenten/Occupational and interdisciplinary skills | |
| Voraussetzung für/Prerequisite for | |
| Präsenzpflicht/Compulsory presence | |
| Plätze/Number of participants | 20 |
| Gruppengröße/Group size | |
| Materialien/Materials | keine / none (optimally, people have their laptop that they can bring to the practicals) |
| Literatur/Literature | will be announced in the first lecture |
| Links | www.czuppon.net/teaching |
| Sonstiges/Further information |
Modulelemente:
Elemente of the module:
| Titel/Title | Zeit (von...bis)/Time (from...to) | Ort(Raum)/Location | |
|---|---|---|---|
| Übungen/Practical exercises | |||
| Vorlesung/Lecture | |||
| Seminare/Semeinars | |||
| Exkursionen/Excursions |
= Modul gehört zum SPP Imoplant / Module is part of the SSP Imoplant
= Modul gehört zum SPP Evolution /Module is part of the SSP Evolution
= Modul gehört zum SPP Bioanalytics and Biochemistry /Module is part of the SSP Bioanalytics and Biochemistry
= Modul gehört zum SPP Neuroscience and Behaviour /Module is part of the SSP Neuroscience and Behaviour
= Modul gehört zum SPP Quantitative Cell Biology /Module is part of the SSP Quantitative Cell Biology