Difference between revisions of "Workshops/BCH2024 2017"
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| − | This is a new course module and | + | This is a new course module and information on this page is currently under development. If you have questions please contact me. |
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;Thursday, January 12 2017 – Data | ;Thursday, January 12 2017 – Data | ||
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;Friday, January 13 2017 – Feature extraction | ;Friday, January 13 2017 – Feature extraction | ||
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===General=== | ===General=== | ||
| − | + | The amount and density of material requires a very significant time comment. | |
*'''Do not take this course''' if you can't devote time over the Christmas break to go through a series of introductory tutorials. | *'''Do not take this course''' if you can't devote time over the Christmas break to go through a series of introductory tutorials. | ||
*'''Do not take this course''' if you can't dedicate the full two weeks from January 13 to 23 to it. You need to free your calendar from conferences, lab-presentations, committee meetings and the like. | *'''Do not take this course''' if you can't dedicate the full two weeks from January 13 to 23 to it. You need to free your calendar from conferences, lab-presentations, committee meetings and the like. | ||
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(Details and links to be announced) | (Details and links to be announced) | ||
| − | * | + | * Work through the [[R_tutorial|'''Introductory R Tutorial]] – this includes installation of '''R''', RStudio and git. |
| − | * | + | * Read '''[[Netiquette]]''' for the course mailing list. |
| − | * | + | * Read [http://adv-r.had.co.nz/Reproducibility.html "How to write a reproducible example"] and [http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example "How to make a great R reproducible example"]. |
| + | * (Some introductory tutorials to bioinformatics still to be posted) | ||
Revision as of 10:53, 19 December 2016
BCH2024 - Biological Data Analysis with R
This is a new course module and information on this page is currently under development. 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 data, subsetting, filtering; designing data models)
- Friday, January 13 2017 – Feature extraction
- (descriptive statistics; dimension reduction)
- Monday, January 16 2017 - Modeling
- (linear and non-linear regression; correlation)
- 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)
Each class meeting will have substantial, required pre-reading and will be complemented with extensive assignments.
General
The amount and density of material requires a very significant time comment.
- Do not take this course if you can't devote time over the Christmas break to go through a series of introductory tutorials.
- Do not take this course if you can't dedicate the full two weeks from January 13 to 23 to it. You need to free your calendar from conferences, lab-presentations, committee meetings and the like.
- Do not take this course if you can't be present for all six class sessions. We are on a tight schedule for evaluations and there will be no make-up opportunities.
Please realize that all available course spots are booked and there is a waiting list. Do not block space that will prevent others from taking the course if you have any doubt that you will take this course in it's entirety.
I repeat: do not enrol in this course if there is any chance you will drop it. This is going to be a hard course with a heavy workload. Be fair to others.
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)
- Work through the Introductory R Tutorial – this includes installation of R, RStudio and git.
- Read Netiquette for the course mailing list.
- Read "How to write a reproducible example" and "How to make a great R reproducible example".
- (Some introductory tutorials to bioinformatics still to be posted)
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
