What you'll 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

Course description

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.

HarvardX has partnered with DataCamp for all assignments. This allows students to program directly in a browser-based interface. You will not need to download any special software, but an up-to-date browser is recommended.

This course is part of the HarvardX Data Science Professional Certificate program.

Faculty

  • Portrait of Rafael Irizarry
    Professor of Biostatistics, T.H. Chan School of Public Health

Associated Schools

  • Harvard T.H. Chan School of Public Health

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