STIN100 Biological Data Analysis

Biology has become a data-rich science with datasets that can no longer be analyzed manually. To extract knowledge from data, biologists need knowledge and skills in programming and data analysis that enable them to explore, visualize and interpret data. This must be done reproducibly, so that it is clear how the data has been processed and easy to modify the analyses if desired.

This course provides basic skills in the programming language R and introduces the student to common methods for visualization and analysis of multi-dimensional biological data. The course is organized around supervised student groups analysing relevant data sets.

In a time when trust in scientific knowledge is no longer obvious, yet challenges of sustainability require informed decisions, the understanding of data and verifiable production of knowledge are essential. STIN100 helps ensure that future employers and decision makers can rely on the knowledge basis prepared by our graduates.

KNOWLEDGE: The students will acquire

• broad knowledge in handling, visualizing and analysing multidimensional biological data.
• familiarity with how some of the most important biological data sets are generated and how this data should be preprocessed to correct for systematic errors.
• a conceptual framework for mapping data to graphical elements.
• a repertoire of programming techniques and concepts that are required to perform the analyses in the course.

SKILLS: Students will be able to

• explain principles behind basic methods for data visualization and analysis.
• write programs that perform basic data processing tasks (subsetting, transformation and groupwise summaries) and employ simple visualization and data analysis methods.
• generate reproducible, executable reports that weave together expository text, program code and output.
• propose biological interpretations of analysis results.
• efficiently search documentation and internet resources to realize analyzes.
• simplify data sets for prototyping and debugging of analyzes.

COMPETENCES: Students will be well prepared to

• explore datasets they encounter in later term papers, theses and working life.
• perform reproducible research where data processing is fully documented through executable reports.
• compose data graphics using element appropriate to the data types and the biological structure in the data.
• pose follow-up questions to data analyses for discussion with domain experts.
• learn new methods and software packages with the help of documentation, code examples and web resources.