HarvardX: Data Science: R Basics course Syllabus

Overview: This course provides a beginner-friendly introduction to the R programming language, emphasizing foundational skills essential for data science. Designed by Harvard faculty, it covers core concepts including data types, data structures, manipulation, and basic programming in R. The curriculum is structured into five modules with hands-on practice, culminating in a final project. Expect to spend approximately 8–10 hours per week over 8 weeks to complete all content and activities.

Module 1: Introduction to R and Programming Basics

Estimated time: 10 hours

• What is R and why it is used in data science
• Setting up the R environment
• Understanding R syntax and basic commands
• Working with variables and functions
• Performing basic operations in R

Module 2: Data Types and Data Structures

Estimated time: 14 hours

• Introduction to vectors and vector operations
• Creating and using matrices
• Working with lists and named elements
• Understanding data frames and their structure
• Indexing and subsetting data efficiently

Module 3: Data Manipulation and Exploration

Estimated time: 14 hours

• Performing basic data cleaning tasks
• Transforming and reshaping datasets
• Summarizing data using descriptive statistics
• Identifying patterns and anomalies in data
• Exploring real-world datasets in R

Module 4: Programming Practice for Data Science

Estimated time: 10 hours

• Writing reusable R scripts
• Creating simple functions in R
• Understanding common programming patterns in data analysis
• Organizing code for clarity and efficiency

Module 5: R in the Data Science Workflow

Estimated time: 8 hours

• Understanding how R fits into data science pipelines
• Integrating R with other tools and workflows
• Preparing for advanced topics in statistics and visualization

Module 6: Final Project

Estimated time: 12 hours

• Load and inspect a real-world dataset
• Perform data cleaning and summarization
• Write a report using R Markdown summarizing findings

Prerequisites

• Familiarity with basic mathematical concepts
• No prior programming experience required
• Access to a computer with R and RStudio installed

What You'll Be Able to Do After

• Understand and use core R programming constructs
• Work confidently with vectors, data frames, and other data structures
• Manipulate and explore datasets using R
• Write simple scripts and functions for data analysis
• Build a foundation for advanced data science work in R