Search result: Catalogue data in Autumn Semester 2017
Agricultural Sciences Bachelor | ||||||
Bachelor Studies (Programme Regulations 2010) | ||||||
5. Semester | ||||||
Methodical Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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751-1010-00L | Introduction to Scientific Methods Part II: Scientific Writing | O | 2 credits | 4G | B. Studer, M. Barthel, B. Dorn, A. K. Gilgen, M. C. Härdi-Landerer, J. Helfenstein, A. Hofmann, R. Kölliker, S. Marquardt, M. Meraner, A. Oberson Dräyer, E. A. Pérez Torres | |
Abstract | Die Studierenden kennen die Grundlagen und die Konventionen des wissenschaftlichen Schreibens in den Naturwissenschaften, können wissenschaftliche Literatur suchen und verwalten sowie wissenschaftliche Publikationen analysieren. Sie setzen das Gelernte beim Schreiben eines eigenen Textes um. | |||||
Objective | Die Studierenden kennen die Grundlagen und die Konventionen des wissenschaftlichen Schreibens in den Naturwissenschaften. Sie setzen das Gelernte beim Schreiben eines kritischen Literaturberichtes zu einem agrarwissenschaftlichen Thema ihrer Wahl um. Die Lehrveranstaltung bereitet die Studierenden auf weitere schriftliche Arbeiten im Studium der Agrarwissenschaften vor, beispielsweise auf die Bachelor-Arbeit. | |||||
Lecture notes | Es wird ein Skript abgegeben. | |||||
751-0441-00L | Scientific Analysis and Presentation of Data | O | 2 credits | 2G | W. Eugster | |
Abstract | This lecture gives an introduction to the scientific work with data covering all steps from data entry via statistical analyses to producing correct scientific graphical output. Exercises with the data analysis software R (via RStudio) will provide hands-on opportunities to get acquainted with data analysis and presentation. Field data gathered with Prof. E. Frossard will be used. | |||||
Objective | This lecture with exercises gives an introduction to the scientific work with data, starting with data acquisition and ending with statistical analyses as they are often required for a bachelor thesis (descriptive statistics, linear regression etc.). Getting data organized with a spreadsheet program (LibreOffice, Excel) and then transfering them to the open-source R package will be the primary focus. An imporant aspect will be to learn which graphical representation of data are best suited for the task (how can data be presented clearly and still scientifically correct?) | |||||
Content | Tentative Programme: 1. Introduction 2. Data acquisition, data organization, data storage, working with data 3. Graphical presentations I - Spreadsheets 4. Preparation of own data from field course with Prof. E. Frossard / 4. Sem. 5. Correct and problematic graphical data displays 6. Introduction to 'R' 7. Data import and graphical presentation 8. Statistical distribution and confidence intervals 9. Statistical tests - Repetition and hands-on applications 10. Linear regressions 11./12. Analysis of Variance 13. ANOVA - Discussion of results with Prof. E. Frossard Last week of semester: examination (Leistungskontrolle) | |||||
Lecture notes | Mainly German (with some English passages from text books) | |||||
Prerequisites / Notice | Theoretical background in ensemble statistics from the mandatory course in the 4th semester; students should have cleared the examination of that fundamental course to be able to follow |
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