have advanced skills in statistical programming in the R language
have advanced skills in data wrangling and graphical data exploration techniques
have advanced skills in linear models and their extensions
have advanced skills in different types of model selection
be able to apply the acquired methods and techniques to their own data
be able to critically evaluate and improve other people’s code
This course will introduce the students to working with real-life data common and statistical methods used in evolution and ecology. The course focuses on developing robust and repeatable workflows for data wrangling, data visualisation and statistical analysis. The students will get an introduction to basic linear models, mode selection and more advanced methods such as Generalised Linear Models and Mixed Models. Throughout the course students will use the programming language R, the RStudio package, and Git. The students will organize and supervise an R coding club throughout the duration of the course.
Classes will be a mixture of lectures, practical exercises, and self-study, designed to build required skills for future modules and to perform analyses frequently encountered in the biological literature. Instruction will be given in English. More information will be given in Canvas at the start of the course.
The estimated student workload in this course is 135 hours.
Approval of reports from all compulsory learning activities. Details are given in Canvas by the start of the course.
Assessment methods and criteria
Portfolio including 1) three coding and statistics challenges related to the course contents and 2) an individually written report including data analysis and interpretation of a given data set. Graded pass/fail. More information will be given in Canvas at the start of the course.
Emneansvarlig fastsetter i samråd med studenttillitsvalgt evalueringsform og om emnene skal ha midtveis- eller sluttevaluering i tråd med kvalitetssystemet kapittel 4.1. Informasjon om evalueringsform for emnet publiseres i Canvas.