Will I earn university credit for completing the Specialization?

Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

What will I be able to do upon completing the Specialization?

You’ll have a foundational understanding of the field and be prepared to continue studying data science.

Can I sign up for the course without paying or applying for financial aid?

Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Data Science: Foundations using R Specialization

Data Science: Foundations using R Specialization

Instructors: Roger D. Peng, PhD

Included with Coursera Plus

Learn more

5 course series

Get in-depth knowledge of a subject

4.6

(6,178 reviews)

Beginner level

No prior experience required

4 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 course series

Get in-depth knowledge of a subject

4.6

(6,178 reviews)

Beginner level

No prior experience required

4 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Use R to clean, analyze, and visualize data.
Learn how to ask the right questions, obtain data, and perform reproducible research.
Use GitHub to manage data science projects.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

35 practice exercises

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

Learn more about Coursera for Business

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Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Johns Hopkins University

Specialization - 5 course series

Ask the right questions, manipulate data sets, and create visualizations to communicate results.

This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.

The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning.

Applied Learning Project

In taking the Data Science: Foundations using R Specialization, learners will complete a project at the ending of each course in this specialization. Projects include, installing tools, programming in R, cleaning data, performing analyses, as well as peer review assignments.

The Data Scientist’s Toolbox

Course 117 hours

What you'll learn

Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts use
Explain essential study design concepts
Create a Github repository

Skills you'll gain

Data Science, R (Software), Version Control, Rmarkdown, R Programming, Data Analysis, Statistical Programming, Exploratory Data Analysis, Software Installation, Data Literacy, and GitHub

R Programming

Course 257 hours

What you'll learn

Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler

Skills you'll gain

R Programming, Simulations, Debugging, Performance Tuning, Data Import/Export, Data Structures, Data Analysis, Program Development, Statistical Programming, Computer Programming Tools, Statistical Analysis, and Programming Principles

Getting and Cleaning Data

Course 319 hours

What you'll learn

Understand common data storage systems
Apply data cleaning basics to make data "tidy"
Use R for text and date manipulation
Obtain usable data from the web, APIs, and databases

Skills you'll gain

Data Manipulation, Data Import/Export, R Programming, Data Cleansing, Data Wrangling, Application Programming Interface (API), SQL, Data Management, Data Collection, MySQL, and Web Scraping

Exploratory Data Analysis

Course 455 hours

What you'll learn

Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
Make graphical displays of very high dimensional data
Apply cluster analysis techniques to locate patterns in data

Skills you'll gain

Ggplot2, R Programming, Plot (Graphics), Exploratory Data Analysis, Statistical Visualization, Histogram, Scatter Plots, Dimensionality Reduction, Box Plots, Unsupervised Learning, Statistical Methods, Data Visualization Software, and Data Analysis

Reproducible Research

Course 57 hours

What you'll learn

Organize data analysis to help make it more reproducible
Write up a reproducible data analysis using knitr
Determine the reproducibility of analysis project
Publish reproducible web documents using Markdown

Skills you'll gain

Knitr, Rmarkdown, R Programming, Data Sharing, Data Validation, General Science and Research, Technical Communication, Version Control, Data Analysis, Exploratory Data Analysis, and Statistical Reporting

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Roger D. Peng, PhD

Johns Hopkins University

37 Courses1,661,574 learners

Brian Caffo, PhD

Johns Hopkins University

30 Courses1,690,266 learners

Offered by

Johns Hopkins University

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Frequently asked questions

Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.

Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).

Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.

Data Science: Foundations using R Specialization

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

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

Advance your subject-matter expertise

Specialization - 5 course series