Joshua Payne: Catalogue data in Autumn Semester 2020 |
| Name | Dr. Joshua Payne |
| Department | Environmental Systems Science |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 701-1460-00L | Ecology and Evolution: Term Paper  | 5 credits | 11A | T. Städler, J. Alexander, S. Bonhoeffer, T. Crowther, A. Hall, J. Jokela, J. Payne, G. Velicer, A. Widmer | |
| Abstract | Individual writing of an essay-type review paper about a specialized topic in the field of ecology and evolution, based on substantial reading of original literature and discussions with a senior scientist. | ||||
| Learning objective | - Students acquire a thorough knowledge on a topic in which they are particularly interested - They learn to assess the relevance of original literature and synthesize information - They make the experience of becoming "experts" on a topic and develop their own perspective - They practise academic writing according to professional standards in English | ||||
| Content | Topics for the essays are proposed by the professors and lecturers of the major in Ecology and Evolution at a joint meeting at the beginning of the semester (the date will be communicated by e-mail to registered students). Students will: - choose a topic - search and read appropriate literature - develop a personal view on the topic and structure their arguments - prepare figures and tables to represent ideas or illustrate them with examples - write a clear, logical and well-structured text - refine the text and present the paper according to professional standards In all steps, they will benefit from the advice and detailed feedback given by a senior scientist acting as personal tutor of the student. | ||||
| Lecture notes | Reading of articles in scientific journals | ||||
| 701-3001-00L | Environmental Systems Data Science | 3 credits | 2G | L. Pellissier, J. Payne, B. Stocker | |
| Abstract | Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses. | ||||
| Learning objective | The students are able to ● frame a data science problem and build a hypothesis ● describe the steps of a typical data science project workflow ● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models ● critically think about the limits and implications of a method ● visualise data and results throughout the workflow ● access online resources to keep up with the latest data science methodology and deepen their understanding | ||||
| Content | ● The data science workflow ● Access and handle (large) datasets ● Prepare and clean data ● Analysis: data exploratory steps ● Analysis: machine learning and computational methods ● Evaluate results and analyse uncertainty ● Visualisation and communication | ||||
| Prerequisites / Notice | 252-0840-02L Anwendungsnahes Programmieren mit Python 401-0624-00L Mathematik IV: Statistik 401-6215-00L Using R for Data Analysis and Graphics (Part I) 401-6217-00L Using R for Data Analysis and Graphics (Part II) 701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften | ||||