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At the end of this course you can:
- describe the properties of eukaryotic genome organization;
- describe the advantages and limitations of next-generation sequencing and list applications within different biological domains;
- list major genomic data sources and use them to search and retrieve relevant information;
- describe the properties of well-formatted data and common problems with messy data and apply this knowledge to clean raw data sets to make them suitable for analysis;
- summarize large datasets using descriptive statistics and effective visualizations, and interpret the results;
- compare and contrast hypothesis-driven and data-driven (exploratory) analysis and you can formulate a testable hypothesis for a specific data set;
- describe the differences between parametric and non-parametric statistical tests, can choose and apply an appropriate test for specific hypothesis and can apply and interpret a correlation or linear regression analysis;
- explain the concept of multiple testing correction, apply corrections and interpret the results;
- define the concept of open data and describe issues, considerations and benefits involved in data sharing.
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- exam, counts for 30%, minimum grade 5.0
- computer assignment, counts for 30%, minimum grade 5.0
- group assignment, counts for 40%, minimum grade 5.0
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The group work in this course uses Team-Based Learning (TBL), an evidence-based collaborative learning strategy. It consists of the following steps: 1) students prepare material before class, 2) students complete and individual and a team-based "readiness assurance test" (iRAT and tRAT) 3) knowledge is applied and extended in application session during the group work.
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