Applied Data Science with R Specialization Course Syllabus

A structured, project-driven specialization that takes beginners from R fundamentals to real-world data science applications. This course spans approximately 15 weeks with hands-on labs and projects, guiding learners through programming, data manipulation, visualization, and machine learning using R. Each module builds practical skills with real datasets, culminating in a capstone project that demonstrates end-to-end data science proficiency. Weekly time commitment varies between 4–6 hours, depending on prior experience.

Module 1: Introduction to R Programming

Estimated time: 8 hours

• R syntax and basic data types
• Writing and executing R scripts
• Control structures: conditionals and loops
• Managing packages and environments

Module 2: Data Wrangling with R

Estimated time: 12 hours

• Loading and inspecting datasets
• Data cleaning with tidyr
• Data transformation using dplyr
• Handling missing values and outliers

Module 3: Data Visualization in R

Estimated time: 12 hours

• Introduction to ggplot2
• Creating bar plots, histograms, and scatterplots
• Customizing plot aesthetics and themes
• Building multi-layered and faceted graphics

Module 4: Machine Learning with R

Estimated time: 16 hours

• Supervised learning: decision trees and random forests
• Unsupervised learning: clustering models
• Model evaluation and validation techniques
• Building and tuning data modeling pipelines

Module 5: Data Science Capstone Project with R

Estimated time: 12 hours

• Problem definition and data selection
• Applying data wrangling and visualization techniques
• Training and evaluating machine learning models

Module 6: Final Project

Estimated time: 10 hours

• Deliverable 1: Complete analysis of a real-world dataset using R
• Deliverable 2: Interactive visualization dashboard or report
• Deliverable 3: Final presentation and model interpretation

Prerequisites

• Basic computer literacy
• Familiarity with fundamental mathematical concepts
• No prior programming experience required

What You'll Be Able to Do After

• Write efficient R scripts for data analysis
• Perform end-to-end data wrangling using tidyverse tools
• Create publication-quality data visualizations with ggplot2
• Build and evaluate machine learning models in R
• Complete a real-world data science project from start to finish