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
Algorithms for sequence-based Bioinformatics
After having followed this course, the student has a good understanding of algorithms and data structures in genomics used for DNA sequence analysis. The student is able to implement algorithms in python, and can translate methods described in scientific literature into a working implementation. Bioinformatics analyses in genomics aim to compare large sets of genomes
Introduction to Bioinformatics
Over the last decades, a number of technologies to study DNA, RNA, proteins, metabolites and their interactions has been developed. To understand life at the molecular level, they have been applied in numerous biological and biomedical experiments. Much of the resulting data, as well as the knowledge gained in these experiments, are freely available for
Ethics in AI design, AI en ML skills
Life-long learning: Multiple free self-paced courses
Big Data Processing
The term “Big Data” describes datasets that are either too big or change too fast or both to be processed on a single computer. Big Data Processing provides an introduction to systems and algorithms used to process Big Data. The main focus of the course is programming and engineering big data systems; initially, the course