COURSE OUTLINE

Day 1: Foundations & The Tidyverse Mindset

• R & RStudio for 2026: Setting up a modern development environment and Quarto projects.
• The Tidy Data Philosophy: Understanding tibbles and why "tidy" data is the key to efficiency.
• Data Ingestion: Loading data from CSV, Excel, SQL databases, and APIs (readr, haven, httr2).
• Hands-on: Importing a "messy" multi-source dataset and performing initial structural audits.

Day 2: Advanced Data Wrangling

• The Five Verbs of dplyr: filter, select, mutate, summarize, and arrange.
• Relational Data: Mastering joins (left_join, inner_join) to combine disparate data tables.
• Pivoting & Reshaping: Moving between "Wide" and "Long" formats with tidyr.
• Hands-on: Transforming raw transaction logs into a clean, customer-centric analytical base table.

Day 3: The Grammar of Graphics & EDA

• Visualizing with ggplot2: Mapping aesthetics, utilizing geoms, and mastering facets. 
• Statistical EDA: Using visualization to spot outliers, skewness, and multi-collinearity.
• Publication-Ready Themes: Customizing scales, labels, and color palettes for executive presentations. 
• Hands-on: Conducting a deep-dive Exploratory Data Analysis (EDA) on a global socioeconomic dataset.

Day 4: Statistical Inference & Linear Modeling

• Modern Inference: Using the infer package for hypothesis testing and confidence intervals.
• Regression Frameworks: Building linear and logistic models with the parsnip package.
• Feature Engineering: Creating "Recipes" for scaling, dummy encoding, and interaction terms. 
• Hands-on: Building a predictive model to forecast real estate prices or customer churn.

Day 5: Machine Learning with Tidymodels

• The Tidymodels Ecosystem: Integrating rsample for cross-validation and tune for hyperparameter optimization.
• Advanced Algorithms: Deploying Random Forests and XGBoost via a unified interface.
• Model Evaluation: Using yardstick to calculate AUC, RMSE, and F1-scores across folds.
• Hands-on: Training and tuning a high-performance ensemble model to solve a classification challenge.

Day 6: Communication, Dashboards & Deployment

• Reproducible Reporting: Generating HTML and PDF reports using Quarto.
• Interactive Data Apps: Building a basic Shiny dashboard to allow users to filter and explore data.
• Model Interpretability: Using vip to explain feature importance and "Variable Importance."
• Capstone Presentation: Deploying an end-to-end data product: a reproducible report and an interactive predictor.