This course is intended for students in the
Biomedical Research Ph.D. program.
Students in this course will learn the basics of data exploration, presentation, and analysis suitable for biomedical researchers. We will focus on
interpretation of data, and on presenting results in a form suitable for publication in peer-reviewed manuscripts.
Class time:
Monday, Wednesday, and Friday 10-10:50 am.
Class meeting room:
Byrd Biotechnology and Science Center Room 102
Course director and instructor: James Denvir, Ph.D.
Email:
Office:
BBSC 336R
Phone:
304-696-7327
Course instructor: Andrew Nato, Ph.D.
Email:
Office:
BBSC 336F
Phone:
304-696-3562
Course instructor: Sadia Akter, Ph.D.
Email:
Office:
BBSC 336M
Phone:
304-696-3782
This course will use online materials; there is no text book. Class time will typically involve students sharing
material they about which they were confused or had questions, with discussion, and a preview by the instructor(s)
of the material to be covered before the next class.
Students will use the R statistical computing environment
for data analysis and visualization. For help with R and RStudio, use the
R Resources button on the navigation bar, in addition to material provided in class.
We will follow the "Introductory statistics with R" training module
which is part of the online training modules provided
by the WV-INBRE grant.
Unless stated "In Class", you should go through the specified page
before class, and be ready to ask questsions and discuss the material in class time.
The schedule is shown below and will be updated as the course progresses:
| Assignment | Due Date | Model solution | R code |
|---|
Installing R and R studio:
These are some (of very many) free online
tutorials available which make good background reading:
- Tutorials from the University of Edinburgh coding club (start with the two introductory R tutorials)
- Quick-R tutorial
- Introductory R tutorial from Kelly Black at UGA
- Data Carpentry tutorials: for Genomics, or for ecology (the contents are pretty similar for both)
- R for Data Science. An online book on tidyverse, written by the author of the tidyverse package. This is an excellent, in depth coverage of data wrangling and analysis.