Foundations of Global Health Specialization Course Syllabus

Overview: This specialization provides a structured, hands-on introduction to data science using R, designed for beginners seeking practical skills in data analysis, cleaning, visualization, and reproducible research. Organized into five core modules and a final project, the course spans approximately 100 hours of content. You'll set up your data science environment, learn R programming fundamentals, acquire and clean real-world data, perform exploratory analysis, and produce reproducible reports using industry-standard tools like RStudio, Git, GitHub, and RMarkdown. Ideal for those pursuing roles in data analysis or research, this course builds foundational skills essential for future learning in statistics, machine learning, and public health data science.

Module 1: The Data Scientist’s Toolbox

Estimated time: 17 hours

• Set up R and RStudio environments
• Install and configure Git and GitHub
• Understand core data science tools and workflows
• Explore basic study design and data problem framing

Module 2: R Programming

Estimated time: 57 hours

• Write and debug R functions
• Use loops, conditionals, and control structures
• Profile and optimize R code
• Read and write data in various formats using R

Module 3: Getting and Cleaning Data

Estimated time: 20 hours

• Access data from web sources, APIs, and databases
• Apply data tidying principles to create consistent datasets
• Understand codebooks and metadata documentation
• Process and transform raw data into usable formats

Module 4: Exploratory Data Analysis

Estimated time: 1–2 hours

• Apply visualization techniques to uncover patterns
• Summarize data distributions and relationships
• Use exploratory methods on real-world datasets

Module 5: Reproducible Research

Estimated time: 7–8 hours

• Understand the principles of reproducible research
• Create literate programs using R Markdown
• Publish analysis reports integrating code and results

Module 6: Final Project

Estimated time: 10 hours

• Conduct a complete data analysis using R
• Document data cleaning and processing steps
• Produce a reproducible report using RMarkdown and GitHub

Prerequisites

• Basic computer literacy
• No prior programming experience required
• Access to a computer with internet connection for software setup

What You'll Be Able to Do After

• Set up a professional data science environment with R and RStudio
• Write efficient R code to manipulate and analyze data
• Acquire and clean complex datasets from diverse sources
• Generate insightful visual summaries and exploratory analyses
• Produce fully reproducible research documents using RMarkdown and version control