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Some class resources are on Slack
Using class material: Each week I'll be putting up links to video tutorials. You should review those BEFORE class. We'll use class time for labs, questions and to put that new knowledge to use.
We'll use the textbook, Data Visualization, by Kieran Healy. You can buy it in various forms or read it online. He has additional material here.
As you work in data, you'll hear the phrase, "the map is not the territory" along with citations to the story by Jorge Luis Borges, "On Rigor in Science". This course is not set up in an easy linear way because that's not how knowledge works -- we may lure in newbies with that fiction but this is grad school. You have a choice about how deep you want to go.
Here are final videos, these are minis,
L 10-1 mini on multi level modeling ,
L 10-2 trees and forests ,
L 10-3 quantiles ,
L 10-4 neural nets
In class Apr 11 we'll do Lab 9
or here is pdf version w pix
Please review this (long) video before class April 11,
L 9-1 Logit and Probit
Homework 8 -- last one!, due midnight of Apr 11
In class Apr 4 we'll do Lab 7
Homework 7, due midnight of Apr 4
Please review these videos before class Apr 4,
L 8-1 Nonlinear ,
L 8-2 interactions
Here are part C of lecture notes
In class Mar 28 we'll do Lab 6
Please review these videos before class March 28,
L 7-1 Dummies ,
L 7-2 Hypothesis Tests in Regressions
about final project
Homework 6, due midnight of Mar 28
In class Mar 21 we'll do Lab 5
Please review these videos before class March 21 (exam is 14th),
L 6-1 Tests of factors ,
L 6-2 OLS ,
L 6-2 mutliple regressions ,
Homework 5, due midnight of Mar 7
In class Feb 29 we'll do Lab 4
Homework 4, due midnight of Feb 29
Please review these videos before class Feb 29 (no class on 22, it's a Monday schedule),
L 5-1 Hypothesis Testing ,
L 5-2 more Hypothesis Testing ,
L 5-3 on p-vals ,
L 5-4 on k-nn
Here's the next chunk of lecture notes
In class Feb 15 we'll do Lab 3
Homework 3, due midnight of Feb 15
Please review these videos before class Feb 15,
L 4-1 Continuous RVs ,
L 4-2 Is That Big? ,
L 4-3 Extreme Values
In class Feb 8 we'll do Lab 2
Homework 2, due midnight of Feb 8
Please review these videos before class Feb 8,
L 3-1 probability ,
L 3-2 more probability ,
L 3-3 discrete random variables
In class Feb 1 we'll do Lab 1
Please review these videos before class Feb 1,
L 2-1 Know your data,
L 2-2 spread,
L 2-3 correlation
Here are the first chunk of lecture notes
Homework 1, due midnight of Feb 1
Please review these videos before the first class Jan 25,
L 1-1 Intro ,
L 1-2 R Basics ,
L 1-3 Some Details
and read these,
L1 material ,
L1 a
We'll use this dataset in R format for class
The zip file includes the data as well as some details about how I created it. To begin you will likely only need the RData file.
Syllabus includes more references for learning R
Grading Policy
My expectations of you and your expectations for me
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