Skip to main content

Data Science: Visualization

Provided by Harvard University (HarvardX)
Introductory
See prerequisites
1–2 hours
per week, for 8 weeks
Free

$49 USD for graded exams and assignments, plus a certificate

Learn basic data visualization principles and how to apply them using ggplot2.

Before you start

An up-to-date browser is recommended to enable programming directly in a browser-based interface.

Choose your pace

Self-Paced courses contain assignments without due dates. You can progress at your own speed.

Steady Learners
80% complete in less than 8 weeks
Accelerated Learners
51% complete in less than 4 weeks
Course opens: Jul 16, 2019
Course ends: Jan 2, 2020

What you will learn

  • Data visualization principles
  • How to communicate data-driven findings
  • How to use ggplot2 to create custom plots
  • The weaknesses of several widely-used plots and why you should avoid them

Overview

As part of our Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.

We’ll also be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important.

The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.

Meet your instructors

Rafael Irizarry
Professor of Biostatistics
Harvard University

Frequently asked questions

Honor code statement
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

Research statement
By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX's instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University's stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.

Please read the edX Privacy Policy for more information regarding the processing, transmission, and use of data collected through the edX platform.

Nondiscrimination/anti-harassment statement
Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact [email protected] and/or report your experience through the edX contact form.
View Courses
This course is part of:

Earn a Professional Certificate in 2-4 months if courses are taken one at a time.

View the program
  1. 8–16 hours of effort

    Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  2. Data Science: Visualization
  3. 8–16 hours of effort

    Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

  4. 8–16 hours of effort

    Learn inference and modeling, two of the most widely used statistical tools in data analysis.

  5. 8–16 hours of effort

    Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

  6. 8–16 hours of effort

    Learn to process and convert raw data into formats needed for analysis.

  7. 8–16 hours of effort

    Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

  8. 16–32 hours of effort

    Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

  9. 30–40 hours of effort

    Show what you’ve learned from the Professional Certificate Program in Data Science.

Get started in data analysis & statistics

Browse over 200 data analysis & statistics courses
Of all edX learners:
73% are employed
Of all edX learners:
45% have children
Based on internal survey results
396,347 people are learning on edX today