David Dalpiaz
Data ImporterExporter
Hello and welcome to my webzone! I am currently a Teaching Assistant Professor for the Department of Statistics at the University of Illinois UrbanaChampaign.
What am I working on?
 Teaching STAT 385, a statistical programming course.
 Writing Atomic R, a textbook introducing the R programming language.
 Developing
bbd
, an R package for accessing baseball data.  Contributing to the PrairieLearn R autograder.
 Researching accessibility needs and best practices in STEM education.
 Applying statistical methodology to public baseball data.
Contact
The best way to reach me is by email. I have a work phone, but I honestly don’t know the number and I respond faster to email anyway.
 Email: dalpiaz2@illinois.edu
 Office: Who knows?
 GitHub: daviddalpiaz
Blog

20230209 Reduce Friction, Increase Learning

20221004 Accessing Baseball Data for Analysis with R

20220402 Recreating NFL Scorigami with R

20200817 Ten Simple Rules for Success in STAT 432

20191127 Letters of Recommendation

20190822 The Extended Syllabus
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Teaching
University of Illinois UrbanaChampaign
 STAT 100 Statistics
 STAT 200 Statistical Analysis
 STAT 212 Biostatistics
 STAT 385 Statistical Programming Methods
sp23
fa22
sp22
fa21
 STAT 400 Statistics and Probability I
sp18
fa17
 STAT 420 Statistical Modeling in R
su17
 STAT 432 Basics of Statistical Learning
sp21
fa20
sp20
fa19
sp18
fa17
 STAT 510 Mathematical Statistics
sp22
sp21
fa20
 STAT 593 STAT Internship
Ohio State University
Textbooks
 Applied Statistics with R
 Currently in use for STAT 420 both inperson and online at Illinois for the Departments of Statistics and Computer Science.
 R for Statistical Learning / Basics of Statistical Learning
 Perpetually undergoing considerable changes and developments, including an attempt at a complete rewrite. Originally intended to be supplemental notes to ISL in order to provide additional examples in R, the text is starting to become a complete reference text for STAT 432, Basics of Statistical Learning, at Illinois for the Department of Statistics. Target audience is advanced undergraduate students in statistics with previous experience with R and regression.