This course consists of two main themes: genomics and data science. The learning outcomes are listed here, organized by the themes.
A. Genomics
At the end of the course you can:
- describe the properties of eukaryotic genomes and explain the mechanisms involved in 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 to characterize nucleotide sequences.
B. Data science
At the end of this course you can:
- 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.
- use R to summarize large datasets using descriptive statistics and effective visualizations, and interpret the results.
- compare and contrast hypothesis-driven and data-driven (exploratory) analysis and 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.
|
 |
|
|
|
|
- Exam, counts for 30%, minimum grade 5.0
- Computer assignment, counts for 30%, minimum grade 5.0
- Team-Based Learning, counts for 40%, minimum grade 5.0
|
|
|
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.
|
|