Data Science: Visualization

About this course

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The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many industries, academia, and government. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, or detect flaws. 

In this course, you will learn the basics of data visualization and exploratory data analysis. We will use three motivating examples and the ggplot2, a data visualization package for the statistical programming language R, to code. To learn the very basics, we will start with a somewhat artificial example: heights reported by students. Then use case studies related to world health and economics and another in infectious disease trends in the United States.

It is also important to note that 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 an error just from the reported results makes data visualization particularly important. This course will explore how failure to discover these problems often leads to flawed analyses and false discoveries.

What you'll learn

• Data visualization principles to better 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