Data Science Notes - Revision history

Adamcolton: /* The Course : 2 */

2013-05-01T16:36:16Z

The Course : 2

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	=== The Course : 2 ===		=== The Course : 2 ===
			Actually working with data is hard. The real world problems - "handling data" - is the hard part, 90% of the problem.

			Working in 'database' software get parallelism and scalability for free.

	=== eScience ===		=== eScience ===
	=== Big Data ===		=== Big Data ===

2013-05-01T16:24:37Z

The Course : 1

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	* schema-on-read		* schema-on-read
	* user defined functions that don't suck		* user defined functions that don't suck

			The Abstractions of DS
			* Matricies and linear algebra
			* relations and relational algebra
			* objects and methods
			* files and scripts
			* data frames and functions

			Current practices don't scale (I'm skipping a lot here because I know the arguments).

	=== The Course : 2 ===		=== The Course : 2 ===

2013-05-01T16:13:35Z

The Course : 1

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	=== The Course : 1 ===		=== The Course : 1 ===
			http://static.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf

			Hadoop was an early MapReduce system that did away with old database standards, but in the 4 years after it's release, most SQL standards were brought back into the fold. The abstractions are important not just the tools.

			MapReduce contributed the following the SQL
			* Fault tolerance
			* schema-on-read
			* user defined functions that don't suck

	=== The Course : 2 ===		=== The Course : 2 ===
	=== eScience ===		=== eScience ===

2013-05-01T16:06:58Z

Dimensions

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	* software engineer → statistical modeling		* software engineer → statistical modeling
	* business analyst → algorithms and tradeoffs at scale		* business analyst → algorithms and tradeoffs at scale

			The Continuums
			* Tools vs Abstractions
			* Desktop vs cloud
			* hackers vs analysts
			* structures vs statistics

	=== The Course : 1 ===		=== The Course : 1 ===

2013-05-01T16:05:18Z

Dimensions

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 === Dimensions ===
 === The Course : 1 ===
 === The Course : 2 ===

2013-05-01T15:24:04Z

Context

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	== Introduction ==		== Introduction ==
	=== Context ===		=== Context ===
			Bill Howe venn diagram. "Danger Zone" is hacking + expertise without statistics.

			Mike Driscolls 3 DS skills
			* Statistics
			* Data Munging
			* Visualization

	=== Dimensions ===		=== Dimensions ===
	=== The Course : 1 ===		=== The Course : 1 ===

2013-05-01T15:10:34Z

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	https://class.coursera.org/datasci-001/class/index		https://class.coursera.org/datasci-001/class/index

			== Introduction ==
			=== Context ===
			=== Dimensions ===
			=== The Course : 1 ===
			=== The Course : 2 ===
			=== eScience ===
			=== Big Data ===
			=== Guest Lecture : Biomedical Informatics ===
			=== Logistics ===

	== Footer ==		== Footer ==
	[[Category:Notes]]		[[Category:Notes]]
	[[Category:Course Work]]		[[Category:Course Work]]

2013-05-01T15:08:08Z

2013-05-01T15:05:29Z

Created page with "https://class.coursera.org/datasci-001/class/index == Footer == Category:Notes Category:Coursera"

New page

https://class.coursera.org/datasci-001/class/index

== Footer ==
[[Category:Notes]]
[[Category:Coursera]]