Big Data Lectures

BUSN 20800 – Spring 2021

All Lecture Materials are listed below in order. The first 7 lectures were designed for html, so the pdf versions are likely to have some iffy formatting.

1. Lecture 1 – Admin, Syllabus, Stats 101, FDR Control – .Rmd file and PDF
2. Lecture 2 – Predictions, FDR Control, and Loss – .Rmd and PDF
   • Lipids example:
     • Code.
     • Data is on canvas under lecture 2. “jointGwasMc_LDL.txt”
   • Diabetes Example:
     • Code: build pvals and do fdr
     • Data: base data and pvals
   • Simulations in lecture: Code
3. Lecture 3 – Regression: Linear and Logit – Rmd and PDF
   • OJ example: code and data
   • Spam example code and data
   • Tidyverse Intro: A brief introduction to tidyverse functions. Rmd (likely to be updated over the next few days)
4. Lecture 4 – Deviance, OOS, Bootstrap – Rmd and PDF
5. Lecture 5 – Variable Selection – Rmd and PDF
   • For those of you struggling with homeworks, there is a lot of valuable code in the old Lecture Rmd files
   • Semiconductors: Data, code in lecture Rmd
   • Comscore: Code in lecture Rmd, Data: domains, sites, total spend
6. Lecture 6 – Cross Validation – Rmd and PDF
   • The last few slides include a lot of useful code. If you can interpret and run the cross-validation code there, you should gain a solid grasp on cross-validation generally.
7. Lecture 7 – Classification – Rmd, and PDF
   • Credit Data and codebook
8. Lecture 8 – Classification 2 – Rmd
9. Lecture 9 – ROC and Trees – Rmd
10. Lecture 10 – Trees and Forests – and Rmd
11. Lecture 11 – Forests and Boosting – Rmd
12. Lecture 12 – Boosting, Ensembles, and Rolling Block CV – Rmd
13. Lecture 13 – Data Cleaning – Rmd
    • Data: cigarettes
14. Lecture 14 – Data Cleaning and Bayes – Rmd
    • Cleaned Zillow Data
15. Lecture 15 – Causal Inference 1: RCTs – Rmd
    • JTPA Data: raw, cleaned, cleaning code
16. Lecture 16 – Causal Inference 2: Targeting, Observational Methods – Rmd
17. Lecture 17 – Neural Nets, SGD, Optimization – Rmd
18. Lecture 18 – Review – Rmd