Workshops/BCH2024 2017
BCH2024 - Biological Data Analysis with R
This is a new course module and all information on this page is tentative and likely to change. If you have questions please contact me.
boris.steipe@utoronto.ca
Contents
The Course
BCH2024 - Biological Data Analysis with R is a module in the Focussed Topics offerings of the Department of Biochemistry. It is an intensive, short-course with a focus on practical data analysis. We will meet in six sessions:
- Thursday, January 12 2017 – Data
- (reading, subsetting, filtering; designing data models)
- Friday, January 13 2017 – Feature extraction
- (descriptive statistics; dimension reduction; information theory)
- Monday, January 16 2017 - Modeling
- (linear and non-linear regression; Bayesian belief networks)
- Thursday, January 19 2017 – Graphs and Networks
- (graph representations; graph metrics)
- Friday, January 20 2017 – Clustering and Classification
- (hierarchical and partition clustering; cluster quality metrics)
- Monday, January 23 2017 – Machine Learning
- (common approaches; cross-validation)
General
This amount of material requires a very significant time comment. Do not take this course if you can't essentially devote the full two weeks to this material.
Coordinator
Dates & Times
Winter term 2017, M R F 17:00 to 19:00.
First class meeting: Thursday, January 12.
Location
- TBD
Prerequisites
Suitable for students without prior programming experience, however you must be willing to put in the time to learn the R programming language within the course.
You must have access to the Internet via your own computer, preferably set up to work through a wireless connection. If at all possible, get a Linux or Mac OSX computer.
Preparation
You need to acquire the basics over the Christmas breaks through a series of tutorials and preparatory reading.
(Details and links to be announced)
- Introduction to R tutorial, including installation of R, RStudio and git.
- Software Engineering Principles
- Introduction to Bioinformatics
Grading and Activities
| Activity | Weight |
| 5 in-class quizzes | 50 marks (5 x 10) |
| Mini-project | 40 marks |
| Participation | 10 marks |
| Total | 100 marks |
Notes
