Standard syllabus
R for data science · Undergraduate · Data science
Topics
R and tidyverse core
- RStudio/Posit workflow: scripts, projects, and packages
- Vectors, lists, factors, and data frames / tibbles
- dplyr verbs: filter, select, mutate, arrange, summarize, group_by
- Joins and relational data with dplyr
- tidyr pivots and nesting (intro)
- Importing CSV and reading from databases (DBI intro)
Visualization and reporting
- ggplot2 layers, aesthetics, and facets
- Themes, scales, and accessible color choices
- Quarto/R Markdown for reproducible reports
- Caching and knitting pitfalls
- Tables with gt or kable (survey)
- Exporting publication-ready figures
Modeling in R
- Formula interface with lm/glm (intro)
- tidymodels or caret survey (course-dependent)
- Train/test splits and resampling (intro)
- Model diagnostics and residual plots
- Classification metrics overview
- Communicating model results to non-R users
Pricing calculator
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Estimates use 15-minute steps (15 minutes to 4 hours). The invoice is based on actual Zoom time (online) or actual session time (in-person), rounded to the nearest 15 minutes. No subscription required.
$60.00 · 60 min · Undergraduate · Online ($60/hr)
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