ST 558 Data Science for Statisticians (R)
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Topic 5: Predictive Modeling
Week 9 Overview
Data Science for Statisticians
Topic 1: Data Science & R Basics
What is Data Science?
Workflows & `Git`/`GitHub` Basics
`Git` & `GitHub` Practice
R
Basics
R projects and Connecting with Github
Quarto Basics
Week 2 Overview
Base R Data Structures: Vectors
Base R Data Structures: Matrices
Base R Data Structures: Data Frames
Base R Data Structures: Lists
Control Flow: Logicals & if/then/else
Control Flow: Loops
Control Flow: Vectorized Functions
Writing Functions
Topic 2: Tidyverse & EDA
Week 3 Overview
Packages
Tidyverse Essentials
Reading Delimited Data
Reading Excel Data
Manipulating Data with
dplyr
Manipulating Data with
tidyr
Databases and Basic SQL
SQL Joins
Week 4 Overview
EDA Concepts
Creating Contingency Tables
Barplots & `ggplot2` Basics
Numerical Variable Summaries
Numeric Variable Graphs
Week 5 Overview
Topic 3: Better Programming
Recap and Direction!
Apply Family of Functions
purrr
Package
Advanced Function Writing
Querying APIs
Topic 4: R Shiny Dashboards
Introduction to R Shiny
Tutorials to Complete Part I
Connecting the UI and Server
Tutorials to Complete Part II
Reactivity
Tutorials to Complete Part III
Dynamic User Interfaces
Flexible UI Layouts & Dashboards
Useful R Shiny Articles I
Debugging & Other Useful Things
Useful R Shiny Articles II
Topic 5: Predictive Modeling
Week 8 Overview
Modeling Concepts
Prediction & Training/Test Sets
Cross-Validation
Multiple Linear Regression
Modeling with
tidymodels
tidymodels
Tutorial
LASSO Models
Modeling Recap
Week 9 Overview
Logistic Regression Models
Regression & Classification Trees
Ensemble Trees
Topic 6: APIs & Docker
Week 10 Overview
Creating an API in
R
Docker Basics
Building a Docker Image
Dockerizing a Shiny App
Topic 5: Predictive Modeling
Week 9 Overview
Week 9 Overview
Published
2025-06-02
More coming soon!
Modeling Recap
Logistic Regression Models