Difference between revisions of "Workshops/BCH2024 2017"

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<div class="alert">
 
<div class="alert">
  
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
:(reading, subsetting, filtering; designing data models)
+
:(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
:(linear and non-linear regression; Bayesian belief networks)
+
:(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.
  
Please realize that the spots are booked and there is a waiting list. Do not block space that others may need 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.
+
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}}
 +
 +
 +
{{#lst:User:Boris|Office_hours}}
 +
  
 
===Dates & Times===
 
===Dates & Times===
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===Location===
 
===Location===
  
;TBD
+
;Biochemistry Department, Large Seminar Room - MS5231
  
 
{{Vspace}}
 
{{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.
 
* Software Engineering Principles
 
* Introduction to Bioinformatics
 
 
 
  
 
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{{Vspace}}
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<tr class="s2">
 
<tr class="s2">
<td>5 in-class quizzes</td>
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<td>4 in-class quizzes</td>
<td>50 marks <small>(5 x 10)</small></td>
+
<td>40 marks <small>(4 x 10)</small></td>
 
</tr>
 
</tr>
  
 
<tr class="s1">
 
<tr class="s1">
<td>Mini-project</td>
+
<td>5 hand-in tasks</td>
<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''']]
 +
** [[Bioinformatics_Introduction_Function|'''Function''']]
 +
 
 +
}}
 +
 
 +
 
 +
{{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