oliryc/datasciencecoursera

content provided for the "Data Scientist’s Toolbox" class

datasciencecoursera

content provided for the "Data Scientist’s Toolbox" class

Definition of data

• Variables: A measurement or characteristic of an item.
• Qualitative: Country of origin, sex, treatment
• Quantitative: Height, weight, blood pressure

The most important thing in data science is the question. The second most important is the data

Why R?

• R is free
• R is one of the most widely used programming language for data science.
• R has one of the best development enviroments - Rstudio http://www.rstudio.com/

Primary file types - R script: .R Primary file types - R markdown document: .Rmd

Statistial inference

Confounding: what is the variable causing the relationship

variable doesn't change no matter what the text is that you're showing. People see you fix that variable so it can't be a con factor. Another way is, that you can stratify. So suppose you have two website colors, and you want to try out both of those website colors with both phrases, and you want to know which phrase works better. Then what you can do is use both phrases equally on both colors. That way, you've stratified your sample so you have both website colors used equally with both phrases. If you can't do any of the, either of the other two things, if you can't fix a variable or stratify it, then what you can do is randomize it.