Data Scientist's Toolbox - Revision history

Adamcolton: /* Experimental Design */

2015-01-20T16:57:01Z

Experimental Design

← Older revision		Revision as of 16:57, 20 January 2015
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	==== Experimental Design ====		==== Experimental Design ====
			Confounding: hidden variable that effects two observed variables. Look at A→B, what is actually happening is C→B and C. Causes spurious correlation.

			* Sensitivity: Pr(positive result \| positive)
			* Specificity: Pr(negative result \| negative)
			* Positive Predictive Value: Pr(positive \| positive result)
			* Negative Predictive Value: Pr(negative \| negative result)
			* Accuracy: Pr(correct outcome)

	== Footer ==		== Footer ==

2015-01-20T15:59:06Z

What about Big Data

← Older revision		Revision as of 15:59, 20 January 2015
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	==== What about Big Data ====		==== What about Big Data ====
			Doesn't really address this, also, I disagree with his approach. I argue that "Big Data" isn't actually determined by size, but structure. If your data-processing architecture is a automated, dynamic network, you're dealing with big data. Otherwise, you're not.

	==== Experimental Design ====		==== Experimental Design ====

2015-01-19T15:32:35Z

What is data

← Older revision		Revision as of 15:32, 19 January 2015
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	==== What is data ====		==== What is data ====
	Quantative/qualitative.		Quantative/qualitative.

			Data is the second most important thing. The most important thing is the question.

	==== What about Big Data ====		==== What about Big Data ====

2015-01-19T15:32:02Z

What is data

2015-01-19T15:31:47Z

Types of Questions

← Older revision		Revision as of 15:31, 19 January 2015
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	=== Week 3 ===		=== Week 3 ===
	==== Types of Questions ====		==== Types of Questions ====
			Types of analysis
			* descriptive: describe the data, like census
			* exploratory: find relationships (not causation)
			* inferential: use a small sample to say something about a bigger population
			* predictive: use data on some object to predict values for another object (still not causation)
			* causal: find out what happens t one variable when you change another variable (very difficult)
			* mechanistic: understand the exact changes in variables that lead to changes in other variables (very difficult, next to impossible unless system is nearly deterministic)

	==== What is data ====		==== What is data ====
	==== What about Big Data ====		==== What about Big Data ====

2015-01-05T21:29:46Z

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

2015-01-05T21:23:10Z

New page

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