Transform, Analyze, and Report Data with R

Overview

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This beginner-level course is your entry into the world of robust, scalable data analysis with R. Designed for aspiring analysts, you will learn to build sophisticated, end-to-end projects from the ground up. You'll master the "Tidyverse" approach, using dplyr to write clean, pipe-based workflows that merge, filter, and prepare complex raw data for analysis. You will also master automation—the hallmark of a modern analyst. Using R Markdown and knitr, you'll transform static scripts into dynamic reports that automatically update visualizations with new data. Finally, you'll dive into data science by rigorously evaluating predictive models with diagnostic tools such as ROC curves and cross-validation. Through hands-on learnings, you'll leave with a portfolio-ready project and the ability to build efficient, reproducible workflows. No prior R experience is necessary.

Syllabus

Data Pipeline Construction and Transformation

This module introduces the foundational skill of data wrangling in R. Learners will explore the "Tidyverse" philosophy and use the dplyr package to build logical, pipe-based workflows. They will learn to take raw, messy data from multiple sources and transform it into a single, clean, and analysis-ready dataset, mastering the techniques used by professional data analysts to ensure data quality and consistency.

Dynamic Reporting and Parameterization

In this module, learners move from data preparation to communication by mastering automated reporting. They will learn to use R Markdown (.Rmd) and knitr to create dynamic, professional-quality HTML reports. The focus is on parameterization—building reports that can automatically ingest new data files and update all text and visualizations—a key skill for efficient, scalable analysis.

Model Evaluation and Cross-Validation

The final module brings learners into the world of predictive analytics and model validation. Focused on a common business problem (customer churn), this module teaches learners how to robustly evaluate a classification model's performance. They will learn to implement k-fold cross-validation and interpret diagnostic tools such as receiver operating characteristic (ROC) and precision–recall curves to make data-driven decisions about which model is best suited for the task.