Data Management, Reporting and Visualization
A 5-Weekend Instructor-Led Training for Public Health & Applied Research Professionals
Rolling Basis - Course offering for new cohort begins every 6 weeks
Live via Zoom
Data Management, Reporting and Visualization (10-Day Weekend Program)
Duration: 10 Days (5 weekends)
Schedule: Saturdays & Sundays | 12:00–2:00 PM ET (New York Time)
Format: Live via Zoom
Payment Options: Bank Transfer, Venmo, or Zelle accepted.
Course Fee: Starting at $250 depending on Country of participation (Limited scholarships and variable pricing available)
About the Training
This 5-weekend short course provides a practical introduction to data management using R, with a focus on preparing real-world datasets for analysis, reporting, and decision-making.
Participants will learn how to clean, transform, organize, validate, and document data using reproducible workflows commonly used in public health and applied research.
- Live, instructor-led sessions
- Hands-on labs & assignments
- Reproducible workflows
- Final mini-project
- Built-in datasets (bring your own later)
Who Should Register
Designed for advanced undergraduate, graduate students and academics, academics, health researchers, business analysts, and data professionals seeking reproducible research skills.
- No prior R experience is required
- Basic familiarity with spreadsheets
- Understanding of fundamental statistics
Learning Objectives
Clean and structure raw data
Reshape and combine data from multiple sources
Produce publication-quality
Generate reproducible reports
Deploy an interactive data product
Weekly Breakdown
- Overview of ML concepts and workflow
- Data types, features, and labels
- Model training vs testing process
- Using real datasets for exploration (e.g., Palmer Penguins)
- Practical examples in R showing how caret and tidymodels integrate
- Handling missing values, scaling, and encoding
- Feature normalization using both caret and tidymodels
- Real dataset: Heart Disease
- Assignment: create and compare preprocessing pipelines
- Linear and multiple regression models
- Regularization (Ridge, Lasso, Elastic Net)
- Evaluation metrics (RMSE, MAE, R²)
Implementation using both caret and tidymodels
- Logistic Regression & KNN classifiers
- Model performance metrics: Accuracy, ROC, AUC, F1
- Implementation with both caret and tidymodels
Visualization with ggplot2
- Decision Trees, Random Forests, and Boosting (XGBoost)
- Bagging vs Boosting theory and implementation
- Performance evaluation and tuning
- Bonus: Feature importance visualization using vip and DALEX
Each week includes:
🎓 Lecture slides (PowerPoint)
💻 R lab exercises (caret + tidymodels)
📊 Real open datasets
Computing & Tools
All analyses are done in R using packages like caret, tidymodels, ggplot2, and DALEX.
All example files and syntax are downloadable from the course portal.