Joshua Payne: Catalogue data in Autumn Semester 2021

Name Dr. Joshua Payne
DepartmentEnvironmental Systems Science
RelationshipAssistant Professor

NumberTitleECTSHoursLecturers
701-1460-00LEcology and Evolution: Term Paper Restricted registration - show details 5 credits11AT. Städler, J. Alexander, S. Bonhoeffer, T. Crowther, A. Hall, J. Hille Ris Lambers, J. Jokela, J. Payne, G. Velicer, A. Widmer
AbstractIndividual 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.
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
ContentTopics 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 notesReading of articles in scientific journals
701-3001-00LEnvironmental Systems Data Science Restricted registration - show details 3 credits2GL. Pellissier, J. Payne, B. Stocker
AbstractStudents 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.
ObjectiveThe 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 / Notice252-0840-02L 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