STT 3860 - Introduction to Statistical Modeling - Tentative Course Schedule - Spring 2026

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General Notes:

• Please bring a notebook and pencil to every class.

• QUARTO cheat sheet

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Week 1: (Jan 13 & 15)

• Before the first class meeting, read Chapter 1 Why Git? Why GitHub? from Happy Git and GitHub for the useR.

• Read chapter 1 - Software for modeling from Tidy Modeling with R.

• Read as needed Happy Git and GitHub for the useR

• Start Chapters 1 and 2 in Introduction to Statistics in R on DataCamp—Due Jan 19 NLT 5:00 PM

• Sign-up for a free account on GitHub. When you register for a free individual GitHub account, request a student discount to obtain a few private repositories as well as unlimited public repositories. Please use something similar to FirstNameLastName as your username when you register with GitHub. For example, my username on GitHub is alanarnholt. If you have a popular name such as John Smith, you may need to provide some other distinguishing characteristic in your username.

• Send an email to arnholtat@appstate.edu with a Subject line of STT3860 - GitHub Username, and tell me in the body of your email your first name, last name, and your GitHub username.

• Make sure RStudio is set up to communicate with Git by following the directions in HappyGitWithR for introducing yourself to Git.

• Cache your credentials and set up a personal access token (PAT) by following the directions in HappyGitWithR.

• TL;DR the chapters in Happy Git With R — follow this document to Set up Git and GitHub

• VIDEO of the setup process

• Video of how to accept and clone assignments (video is for my STT 3850 class; but, the process will be the same for this class)

• Set Global Options

Optional Resources:

• Miscellaneous RMarkdown Tips

• DataCamp: Communicating with Data in the Tidyverse

• Data Science with R

• DataCamp: Introduction to Git for Data Science

• Reading in Data

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Week 2: (Jan 20 & 22)

• Read the chapter 2 - A Tidyverse Primer from Tidy Modeling with R.

• Tidyverse Primer Notes

• Start Chapters 3 and 4 in Introduction to Statistics in R on DataCamp—Due Jan 23 NLT 5:00 PM

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Week 3: (Jan 27 & 29)

• Read chapter 3 - A Review of R Modeling Fundamentals from Tidy Modeling with R.

• Review of R Modeling Notes

• Start reproduction of Introduction to Statistics in R—Due Feb 2 NLT 5:00 PM

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Week 4: (Feb 3 & 5)

• Finish reproduction of Introduction to Statistics in R—Due Feb 2 NLT 5:00 PM

• Read chapter 4 - The Ames Housing Data from Tidy Modeling with R.

• Read Chapters 1-5 of Correlation and Regression

• Start Chapters 1 and 2 in Modeling with Data in the Tidyverse on DataCamp—Due Feb 6 NLT 5:00 PM

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Week 5: (Feb 10 & 12)

• Read chapter 5 - Spending our Data from Tidy Modeling with R.

• Start Chapters 3 and 4 in Modeling with Data in the Tidyverse on DataCamp—Due Feb 13 NLT 5:00 PM

• Misc Regression topics

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Week 6: (Feb 17 & 19)

• Read chapter 6 - Fitting Models with parsnip from Tidy Modeling with R

• Read Appendix E.

• Start reproduction of Modeling with Data in the Tidyverse—Due Feb 23 NLT 5:00 PM

• In class review this document.

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Week 7: (Feb 24 & 26)

• Finish reproduction of Modeling with Data in the Tidyverse—Due Feb 23 NLT 5:00 PM

• Read chapter 7 - A Model Workflow from Tidy Modeling with R

• Read Multiple and Logistic Regression

• Start Chapters 1 and 2 in Modeling with Tidymodels in R on DataCamp—Due Feb 27 NLT 5:00 PM

• Read Not mtcars AGAIN

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Week 8: (Mar 3 & 5)

• Read chapter 8 - Feature Engineering with recipes from Tidy Modeling with R

• Read Build a model and Preprocess your data with recipes

• Start Chapters 3 and 4 in Modeling with Tidymodels in R on DataCamp—Due Mar 6 NLT 5:00 PM

• Start reproduction of Modeling with Tidymodels in R - Due Mar 19 NLT 5:00 PM

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Spring Break: Mar 9—13

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Week 9: (Mar 17 & 19)

• Read chapter 9 - Judging Model Effectiveness from Tidy Modeling with R

• Work on reproduction of Modeling with Tidymodels in R - Due Mar 19 NLT 5:00 PM

• Directions for Project 1

• Accept Project 1 - Presentations will be March 31.

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Week 10: (Mar 24 & 26)

• Read chapter 10 - Resampling for Evaluating Performance from Tidy Modeling with R

• Work on Project 1 - Presentations will be March 31.

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Week 11: (Mar 31 & Apr 2)

• Group presentations for Project 1—Mar 31

• Read chapter 11 Comparing Models with Resampling from Tidy Modeling with R

• Read Tree Based Methods

• Read Chapters 2-4 in Tree based Models in R

• Start Chapters 1 and 2 in Machine Learning with Tree Based Models in R on DataCamp—Due Apr 6 NLT 5:00 PM

Optional Resources:

• Review as needed Train and Test

• Review as needed Regression Trees

• Review as needed Predictive Model Building

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Week 12: (Apr 7 & 9)

• Read chapter 12 - Model Tuning and the Dangers of Overfitting from Tidy Modeling with R

• Read Chapters 5-6 in Tree based Models in R

• Start Chapters 3 and 4 in Machine Learning with Tree Based Models in R on DataCamp—Due Apr 13 NLT 5:00 PM

• Start reproduction of Machine Learning with Tree Based Models in R—Due Apr 20 NLT 5:00 PM

Optional Resources:

• Review as needed Train and Test

• Review as needed Regression Trees

• Review as needed Predictive Model Building

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Week 13: (Apr 14 & 16)

• Read chapter 13 - Grid Search from Tidy Modeling with R

• Work on reproduction of Machine Learning with Tree Based Models in R—Due Apr 20 NLT 5:00 PM

• Read Stack Overflow

• Read VOTERS

• Read What do nuns think?

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Week 14: (Apr 21 & 23)

• Finish reproduction of Machine Learning with Tree Based Models in R - Due Apr 20 NLT 5:00 PM

• Directions for Project 2

• Accept Project 2 —Group presentations will be May 7 during the final exam period (8 am—10:30 am).

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Week 15: (Apr 28)

• Work on Project 2.

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Final Exam Period (May 7, 8 am—10:30 am)

• Final Group Presentations for Project 2

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Final Exam

• University Exam Schedule

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Last Updated on: Dec 05, 2025 at 01:09:26 PM

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