Difference between revisions of "Workshops/BCH2024 2017"

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== The Course ==
 
== 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 on six sessions:  
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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:  
 
   
 
   
  

Revision as of 18:24, 24 October 2016

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



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

Boris Steipe


 



 

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.


 

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