Across the life sciences, scientists utilize omics data to study biological phenomena in humans, plants, animals and microbes. This results in large and heterogeneous data sets that can be analyzed using a variety of algorithms and statistical methods. Making sense of the data, extracting biological knowledge out of the results of these analyses and formulating new hypotheses and research questions based on them is not trivial. However, when basic data science skills are combined with domain knowledge, either from literature or from databases accessed in a high-throughput manner, omics data constitute a goldmine for data-driven discovery of novel insights and hypotheses that can be tested in follow-up experiments.
This course will train students in linking domain knowledge to data using data science techniques and skills, in order to design omics experiments, evaluate the quality of the resulting data, interpret them in the light of literature and domain databases, and mine them to make discoveries and compose new research questions and hypotheses. Domain-specific case studies will allow students to directly apply their skills on data relevant to their specialization.
