Data Analytics and Visualization Using R

This course teaches the basics and fundamental practices of R programming for data science and bioinformatics, giving students hands-on programming skills to tackle biological questions and approach them with an analytical and critical mindset.

Course abbreviation: IND-R

Instructor: Dr. Mohamed Hamed and Dr. Fadhl Alakwaa, PhD

Research group leader, Rostock University, Germany; Research investigator, University of Michigan, Ann Arbor, USA.

Workload: 12 lectures, 3 hours each. Total workload: 36 hours of lectures and tutorials.

Entrance requirements: None

Used media: Anaconda, Jupyter Notebook, PDF slides, R scripts

Objectives

• Understand fundamental R programming practices for data science and bioinformatics
• Develop hands-on programming skills for biological questions
• Use proper statistical analyses in bioinformatics
• Develop reproducible R analysis pipelines

Competences to be Developed

• Basic programming in R
• Descriptive and inferential statistics
• Exploratory data analysis
• Bioinformatics algorithms and biological data resources
• Jupyter Notebook analysis pipelines in R

Assessment

• Final project applying learned methods and skills
• Project output as an R package, Jupyter Notebook, reproducibility study, or benchmarking study
• Projects should comply with FAIR principles
• Scientific review and presentation after the last lecture