Difference between revisions of "Workshops/BCH2024 2017"

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<div class="alert">
 
<div class="alert">
  
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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.
+
This is a new course module and information on this page is currently under development. If you have questions please contact me.
  
 
<tt>boris.steipe@utoronto.ca</tt>
 
<tt>boris.steipe@utoronto.ca</tt>
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;Thursday, January 12 2017 – Data
 
;Thursday, January 12 2017 – Data
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:(reading, subsetting, filtering; designing data models)
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:(reading data, subsetting, filtering; designing data models)
  
 
;Friday, January 13 2017 – Feature extraction
 
;Friday, January 13 2017 – Feature extraction
−
:(descriptive statistics; dimension reduction; information theory)
+
:(descriptive statistics; dimension reduction)
  
 
;Monday, January 16 2017 - Modeling
 
;Monday, January 16 2017 - Modeling
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:(linear and non-linear regression; Bayesian belief networks)
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:(linear and non-linear regression; correlation)
  
 
;Thursday, January 19 2017 – Graphs and Networks
 
;Thursday, January 19 2017 – Graphs and Networks
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;Monday, January 23 2017 – Machine Learning
 
;Monday, January 23 2017 – Machine Learning
 
:(common approaches; cross-validation)
 
:(common approaches; cross-validation)
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 +
{{Vspace}}
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Each class meeting will have substantial, required pre-reading and will be complemented with extensive assignments.
  
 
{{Vspace}}
 
{{Vspace}}
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===General===
 
===General===
  
−
This amount 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, and dedicate the full two weeks from January 13 to 23 to this course.  
+
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.
  
 
{{#lst:User:Boris|Coordinator}}
 
{{#lst:User:Boris|Coordinator}}
 +
 +
===Contact===
 +
 +
 +
;All contact will be via a Google group.
 +
 +
* To send mail, click here: [mailto:bch2024_2017@googlegroups.com '''mailto:bch2024_2017'''].
 +
* To visit this forum on the Web, click here: [https://groups.google.com/forum/#!forum/bch2024_2017 '''BCH2024_2017'''].
 +
*  Note that this is a list for technical discussions and I expect everyone to follow the standards of communication spelled out here: '''[[Netiquette]]'''.
  
 
{{Vspace}}
 
{{Vspace}}
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 +
 +
{{#lst:User:Boris|Office_hours}}
 +
  
 
===Dates & Times===
 
===Dates & Times===
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===Location===
 
===Location===
  
−
;TBD
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;Biochemistry Department, Large Seminar Room - MS5231
  
 
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{{Vspace}}
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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.
 
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.
−
 
−
{{Vspace}}
 
−
 
−
===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.
 
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* Software Engineering Principles
 
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* Introduction to Bioinformatics
 
−
 
−
 
  
 
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<tr class="s2">
 
<tr class="s2">
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<td>5 in-class quizzes</td>
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<td>4 in-class quizzes</td>
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<td>50 marks <small>(5 x 10)</small></td>
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<td>40 marks <small>(4 x 10)</small></td>
 
</tr>
 
</tr>
  
 
<tr class="s1">
 
<tr class="s1">
−
<td>Mini-project</td>
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<td>5 hand-in tasks</td>
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<td>40 marks</td>
+
<td>50 marks <small>(5 x 10)</small></td>
 
</tr>
 
</tr>
  
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{{Vspace}}
 
{{Vspace}}
  
−
===Notes===
+
*'''Quizzes''' will relate to previous weeks' assignments and current weeks' pre-reading.
 +
*'''Hand-in tasks''' will cover preparatory tasks and exploratory perspectives; contents to be discussed in class.
 +
*'''Participation''' means active, well-prepared contributions to discussions in-class and on the mailing list.
 +
 
 +
{{Vspace}}
 +
 
 +
==Preparation==
 +
 
 +
You need to acquire the basics over the holidays through a series of tutorials and preparatory reading.
 +
 
 +
{{task|1 =
 +
 
 +
* 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"].
 +
* In RStudio, create a '''New Project''', cloned from a GitHub repository. The repository URL is <code><nowiki>https://github.com/hyginn/R_Exercise-Bioinformatics</nowiki></code>. Create this in the same way as you created the <code>R_Exercises-BasicSetup</code> project for the [[R_tutorial#Git_Version_control|'''R'''-tutorial]]. The scripts in that project are loosely interleaved with the introductory tutorials to bioinformatics below.
 +
* Work through the following introductory tutorials to bioinformatics (to be updated):
 +
** [[Bioinformatics_Introduction_Data|'''Data''']]
 +
** [[Bioinformatics_Introduction_Sequence|'''Sequence''']]
 +
** [[Bioinformatics_Introduction_Structure|'''Structure''']]
 +
** [[Bioinformatics_Introduction_Phylogeny|'''Phylogeny''']]
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** [[Bioinformatics_Introduction_Function|'''Function''']]
 +
 
 +
}}
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 +
 
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{{Vspace}}
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==Notes==
 
<references />
 
<references />
  

Latest revision as of 20:07, 13 January 2017

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



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

Boris Steipe


 


Contact

All contact will be via a Google group.
  • To send mail, click here: mailto:bch2024_2017.
  • To visit this forum on the Web, click here: BCH2024_2017.
  • Note that this is a list for technical discussions and I expect everyone to follow the standards of communication spelled out here: Netiquette.


 



Office hours

(Virtual) face to face meetings are by appointment, if required. However, we will be able to resolve almost all issues by e-mail. You will find that discussions by e-mail are both more efficient and effective than meetings. Moreover e-mail discussions leave you with a document trail of what was discussed, can contain links to information sources, and we can share points of general interest more easily with the class.


 



Dates & Times

Winter term 2017, M R F 17:00 to 19:00.

First class meeting: Thursday, January 12.


 

Location

Biochemistry Department, Large Seminar Room - MS5231


 

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
4 in-class quizzes 40 marks (4 x 10)
5 hand-in tasks 50 marks (5 x 10)
Participation 10 marks
Total 100 marks


 
  • Quizzes will relate to previous weeks' assignments and current weeks' pre-reading.
  • Hand-in tasks will cover preparatory tasks and exploratory perspectives; contents to be discussed in class.
  • Participation means active, well-prepared contributions to discussions in-class and on the mailing list.


 

Preparation

You need to acquire the basics over the holidays through a series of tutorials and preparatory reading.

Task:


 

Notes




 
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