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.
Instructor
Associated Schools
Harvard T.H. Chan School of Public Health
You may also like
- Build a foundation in R and learn how to wrangle, analyze, and visualize data.FreeAvailable now8 weeks
- Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.FreeAvailable now8 weeks
- Learn inference and modeling: two of the most widely used statistical tools in data analysis.FreeAvailable now8 weeks
