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Transform, Analyze, and Report Data with R

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Transform, Analyze, and Report Data with R

This course is part of multiple programs.

Instructor: LearningMate

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Build an end-to-end, reproducible analysis in R, from data transformation and model evaluation to automated reporting.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

Module details

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.

What's included

2 videos1 reading2 assignments

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.

What's included

2 videos1 reading2 assignments

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.