Compositional Data Analyis
| 2024_HS_ComDaAn | |
| 13.01.2025 - 15.01.2025 | |
| 3 days | |
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Beginn Anmeldefrist: 01.06.2024 Ende Anmeldefrist: 13.12.2024 |
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| ETH Zürich, Zentrum | |
| 2 | |
| 10 | |
| Students enrolled in PSC PhD Programs: CHF 0 LSZGS PhD students: CHF 0 All others: CHF 300 | |
| ETH, tba | |
| Compositional data analysis is a methodology used to describe the parts/compounds of a whole, conveying relative information. Typical examples in different fields are: geology (geochemical elements), medicine (body composition: fat, bone, lean), food industry (food composition: fat, sugar, etc), chemistry (chemical composition), ecology (abundance of different species), agriculture (nutrient balance ionomics), environmental sciences (soil contamination), plant science (water, carbon and nitrogen content, composition of soil or microbial communities, species composition) and genetics (genotype frequency). This type of data appears in most applications, and the interest and importance of consistent statistical methods cannot be underestimated. Compositional data analysis is the solution to the problem of how to perform a proper statistical analysis of this type of data i.e., to solve the problem of spurious correlation as it was named by Karl Pearson. This course will provide an overview of compositional data analysis, drawing on the fundamental concepts from the referenced book (https://doi.org/10.1007/978-3-319-96422-5), but will tailor the complexity to suit applications in plant sciences, offering a more accessible approach to the subject matter. | |
| Prof. Dr. Matthias Templ, FHNW School of Economics | |
| 1 | |
| PhD students Postdocs if places available | |
| Participants should bring their laptops to the exercises with the R software environment and a suitable editor (e.g. RStudio) installed. It is assumed that students enrolling in this course have successfully completed a fundamentals of data science or statistics course and are familiar with programming (preferably in R). | |
| English | |
| Students will actively participate during the course. On the third day, students are expected to complete a practical assignment including a presentation in their group. Using a case/data set from their own research area or dissertation project, they will be able to apply some of the concepts learned in the course. A draft report, submitted 3 weeks after the course and incorporating the presentation discussions, should summarize the findings in approximately 3 pages. | |
By registering you agree to the PSC course terms and conditions AGBs | |
| Cancellation of a course registration should be arranged with the course coordination office psc_phdprogram@ethz.ch and is possible free of charge up to 2 weeks before the course starts. Later cancellations and failure to attend or incomplete attendance without documented justification will incur a fee of 200 CHF. Cancellations should be arranged with the PSC coordination office psc_phdprogram@ethz.ch. Incomplete attendance without documented justification will be charged with CHF 60 per day missed. | |
| Participants should bring their laptops to the exercises with the R software environment and a suitable editor (e.g. RStudio) installed. Registration Important: ETH PhD students have to register via MyStudies only, to ensure valid registration ! | |
| Dr. Bojan Gujas (psc_phdprogram@ethz.ch) | |
| BG_HS24_Compositional Data Analyis.pdfvertical_align_bottom |