Bioinformatics and Genomics
In this course, attention is paid to understanding and working with large amounts of data as has been obtained in recent years with genetic and molecular research. These technological developments require new skills and concepts to be able to understand and conduct life science research. Successively work with mutations and sequencing data will be used.
The Science of Food Systems
The Science of Food Systems course is an introductory course that takes place in the third period of the first year of the Bachelor’s programme Global Sustainability Science. In 2024 the course will run for the first time from February (week 6) to April (week 15), during 10 weeks. This is a track-specific course embedded
Exploratory Data Analysis in R
lant scientists use a wide range of experimental tools. Be it a simple weight measurement or an advanced imaging analysis, every experiment generates data that must be analyzed. Modern approaches for reproducible data analysis are mostly based on writing scripts in (statistical) programming languages. As such, working in the plant sciences and broader life sciences
Genome Bioinformatics
Complex biological systems arise from interactions between molecules, cells, organisms, and their environment. The genome serves as the blueprint for these interactions and is shaped by 4 billion years of evolution. With advances in sequencing technologies, researchers can now collect large-scale genomic datasets from species, populations, and environments. In this course, students explore how such
Data Analysis & Visualization
Much data is quantitative, and there is a wide range of methods available for the analysis of such data. After a brief introduction to data types and normalisation, a number of visualisation methods will be discussed. Next, methods will be introduced to find groups (clustering), dependencies (regression), significant differences between conditions (hypothesis testing) and to
Fundamentals of Data Science
Data science is a dynamic and fast-growing interdisciplinary research field that, across science, industry, and government, is altering how people understand the world and make decisions. Not surprisingly, the demand for data science skills is on the rise. This course will cover key principles and tools of data science. In particular, the course will cover
Advanced Bioinformatics (WUR)
This course covers the process of bioinformatics data analysis and the interpretation of the results in a biological context. The following topics will be addressed in the course: command line usage, programming/scripting, current bioinformatics data analysis tools, and automated analysis pipelines. The first part of the course will cover command line usage (linux), bioinformatics script
Programming in Python
Programming plays an important role in many domains. In business and science writing or adapting computer programs to process, analyse and visualize data in a suitable format has become common practice. This course aims to help students to understand the underlying principles of programming and equip them with basic skills to create computer programs. The
Machine Learning 1
This course is lecture based, with homework assignments and programming assignments. The curriculum is based on chapters 1,2,3,4,5,6,7,9,14 of the book Pattern Recognition and Machine Learning by C. Bishop: Statistical learning principles; Linear regression; Linear classification; Neural networks; Kernel methods; Dimensionality reduction; Clustering methods; Ensemble methods
Plant-Microbe Interactions
Plant-microbe interactions will be introduced in a general lecture, highlighting recent developments and the importance of the reviewing process. For a set of recent manuscripts the students will act as reviewers and editors following the format provided by high standard international journals. Editor decisions will be presented and discussed.