Data Science: Foundations using R Specialization

Foundations of Global Health Specialization

A practical and reputable launchpad into data science using R—organized, hands-on, and perfect for building future-ready skills.

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access	Lifetime
level	Medium
certificate	Certificate of completion
language	English

Description
Additional information

What will you learn in Data Science: Foundations using R Specialization Course

Learn to clean, analyze, and visualize data using R.
Get comfortable with asking the right questions, accessing data, and ensuring your research is reproducible.

Use GitHub to manage your data science projects and collaborate effectively.
Gain deep skills in using RStudio, version control, RMarkdown, and ggplot2 for data storytelling.

Program Overview

Course 1: The Data Scientist’s Toolbox

⌛ 17 hours

Topics: Set up R, RStudio, Git, and GitHub. Learn basic study-design concepts. Understand data, problems, and tools used in data science.
Hands-on: Create your toolbox. Set up software. Make a GitHub repository and explore essential tools.

Course 2: R Programming

⌛ 57 hours

Topics: Install and configure R environments. Learn R syntax, loops, functions, debugging, and profiling. Read and write data in R.
Hands-on: Complete programming and debugging tasks. Explore data via R functions and code organization. Class

Course 3: Getting and Cleaning Data

⌛ 20 hours

Topics: Acquire data from web, APIs, databases, and other sources. Learn data cleaning and tidying (creating tidy data). Understand datasets, codebooks, and processing steps.
Hands-on: Obtain and clean real data. Create tidy datasets and document the data-processing workflow

Course 4: Exploratory Data Analysis

⌛ 1–2 hours

Topics: Learn visualization and summary techniques. Understand trends, patterns, and relationships in data.
Hands-on: Apply exploratory methods on real-world data. Generate visual summaries using real datasets.

Course 5: Reproducible Research

⌛ 7–8 hours

Topics: Understand reproducible research and its importance. Learn tools like R Markdown for literate programming.
Hands-on: Publish analysis as a single document that includes code and results for reproducibility.

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Job Outlook

A strong foundation for roles like Data Analyst, Junior Data Scientist, or Research Assistant—especially in environments that use R.
Helps bridge into more advanced study or specializations, such as “Data Science: Statistics and Machine Learning.”
Build real-world-ready skills prized at academic and industry levels—like GitHub version control, reproducibility, and tidy data practices.

9.7Expert Score

Highly Recommendedx

A well-rounded, beginner-friendly specialization that lays the groundwork for practical, reproducible data science using R. Ideal for those seeking a strong, structured entry point into the data science pipeline.

Value

9.5

Price

9.3

Skills

9.8

Information

9.7

PROS

Covers all key stages of working with data—from setup and programming to cleaning, exploration, and reproducibility.
Hands-on projects at the end of each course reinforce learning by doing.
Respected faculty from Johns Hopkins University add credibility and teaching quality.

CONS

Many learners report the lectures can feel dry or engineering-heavy, especially early on.
Designed as foundational content—it lacks advanced modeling or machine learning content, which you'll need to pick up later in follow-up specializations.