Coursera - Revision history

Tim at 14:29, 20 February 2013

2013-02-20T14:29:30Z

← Older revision		Revision as of 10:29, 20 February 2013
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	The best thing about the course is being turned on to R and R.Studio and .Rmd. Particularly .Rmd.		The best thing about the course is being turned on to R and R.Studio and .Rmd. Particularly .Rmd.

	.Rmd seems to hold promise as a way for researchers and students to present information. In the course it seems as though Jeff uses it then turns it into stale powerpoint slides.		.Rmd seems to hold promise as a way for researchers and students to explore and present information. In the course it seems as though Jeff uses it then turns it into stale powerpoint slides.

	I would be great to have an .Rmd wiki where we each have some personal area to write our explorations hit save and have them run and create a sweet looking page. The lectures could live there too in a public space as .Rmd rendered files. If you need to be all plagarism/security-conscious then you could keep student work secret till some time each week. Ah but after that it would be cool to explore each others code, add comments, steal it to run in your own code area.		I would be great to have an .Rmd wiki where we each have some personal area to write our explorations hit save and have them run and create a sweet looking page. The lectures could live there too in a public space as .Rmd rendered files. If you need to be all plagarism/security-conscious then you could keep student work secret till some time each week. Ah but after that it would be cool to explore each others code, add comments, steal it to run in your own code area.

Tim at 14:27, 20 February 2013

2013-02-20T14:27:47Z

← Older revision		Revision as of 10:27, 20 February 2013
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	EdX and Coursera have an opportunity to be game changers in education. In Data Analysis it seems we have some of the building blocks already. It could be awesome. Instead we got dumb powerpoint and stupid rubrics and typical paranoia about plagiarism. Same old same old.		EdX and Coursera have an opportunity to be game changers in education. In Data Analysis it seems we have some of the building blocks already. It could be awesome. Instead we got dumb powerpoint and stupid rubrics and typical paranoia about plagiarism. Same old same old.

			Tim McKenna

Tim at 14:25, 20 February 2013

2013-02-20T14:25:53Z

← Older revision		Revision as of 10:25, 20 February 2013
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			Lies, Damn Lies and Statistics

			I kind of hated my paper, and I felt the same way about those I reviewed. I felt like we were being all trained to be the same kind of robot, we all said the the same things like 'if we had more data we would have done even more fantastic at not saying much' using references we never read. But we all covered the bases and we'll get a B or whatever.

			I got little out of it, all form no substance.

			For a moment at the beginning I thought it would actually be interesting to be massively online The seeing each other's code and comments but then it disappeared lost in what seemed like paranoia about cheating or safety or some such.

			The best thing about the course is being turned on to R and R.Studio and .Rmd. Particularly .Rmd.

			.Rmd seems to hold promise as a way for researchers and students to present information. In the course it seems as though Jeff uses it then turns it into stale powerpoint slides.

			I would be great to have an .Rmd wiki where we each have some personal area to write our explorations hit save and have them run and create a sweet looking page. The lectures could live there too in a public space as .Rmd rendered files. If you need to be all plagarism/security-conscious then you could keep student work secret till some time each week. Ah but after that it would be cool to explore each others code, add comments, steal it to run in your own code area.

			What is really exciting would be to extend the .Rmd package to be this generalized calculating, code-running, pretty-formatting workspace. It could be the best thing since wikipedia.

			EdX and Coursera have an opportunity to be game changers in education. In Data Analysis it seems we have some of the building blocks already. It could be awesome. Instead we got dumb powerpoint and stupid rubrics and typical paranoia about plagiarism. Same old same old.




	[[p2pLoans]]		[[p2pLoans]]

Tim at 23:40, 17 February 2013

2013-02-17T23:40:19Z

← Older revision		Revision as of 19:40, 17 February 2013
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			[[p2pLoans]]

	*[http://cran.r-project.org/doc/contrib/Short-refcard.pdf refrence card]		*[http://cran.r-project.org/doc/contrib/Short-refcard.pdf refrence card]
	*[http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html R style guide]		*[http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html R style guide]

Tim at 20:23, 29 January 2013

2013-01-29T20:23:16Z

← Older revision		Revision as of 16:23, 29 January 2013
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			*[http://cran.r-project.org/doc/contrib/Short-refcard.pdf refrence card]
			*[http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html R style guide]



	Define the question		Define the question
	Define the ideal data set		Define the ideal data set

Tim at 19:44, 29 January 2013

2013-01-29T19:44:24Z

← Older revision		Revision as of 15:44, 29 January 2013
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	Synthesize/write up results		Synthesize/write up results
	Create reproducible code		Create reproducible code
			dog

Tim at 19:43, 29 January 2013

2013-01-29T19:43:52Z

← Older revision		Revision as of 15:43, 29 January 2013
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	Define the question		Define the question
	Define the ideal data set		Define the ideal data set
	Determine what data you can access		Determine what data you can access
	Obtain the data		Obtain the data
	Clean the data		Clean the data
	Exploratory data analysis		Exploratory data analysis
	Statistical prediction/modeling		Statistical prediction/modeling
	Interpret results		Interpret results
	Challenge results		Challenge results
	Synthesize/write up results		Synthesize/write up results
	Create reproducible code		Create reproducible code

Tim at 19:43, 29 January 2013

2013-01-29T19:43:11Z

← Older revision		Revision as of 15:43, 29 January 2013
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	Define the question		Define the question
	Define the ideal data set		Define the ideal data set
	Determine what data you can access		Determine what data you can access
	Obtain the data		Obtain the data
	Clean the data		Clean the data
	Exploratory data analysis		Exploratory data analysis
	Statistical prediction/modeling		Statistical prediction/modeling
	Interpret results		Interpret results
	Challenge results		Challenge results
	Synthesize/write up results		Synthesize/write up results
	Create reproducible code		Create reproducible code

Tim: Created page with "Define the question Define the ideal data set Determine what data you can access Obtain the data Clean the data Exploratory data analysis Statistical prediction/modeling Inter..."

2013-01-29T19:42:30Z

Created page with "Define the question Define the ideal data set Determine what data you can access Obtain the data Clean the data Exploratory data analysis Statistical prediction/modeling Inter..."

New page

Define the question
Define the ideal data set
Determine what data you can access
Obtain the data
Clean the data
Exploratory data analysis
Statistical prediction/modeling
Interpret results
Challenge results
Synthesize/write up results
Create reproducible code