While data scientists receive training in mathematics and computer science, their audiences often do not. Results usually need to be communicated to managers, customers, or coworkers. These audiences usually lack the requisite training to understand the statistical information given to them. Data visualizations are useful in simplifying and summarizing the results of data analyses. This unit will cover the skills you need to convey your message through visualizations.
Completing this unit should take you approximately 3 hours.
This video demonstrates some of the more commonly used charts and explains how to use them. Recall the different data types we discussed in Unit 2 and note how each type of data in the video is charted.
These lecture note slides walk through various charting options at your disposal. The slide collection also includes some working examples to show how one dataset can be visualized with many different charts. Take note of which charts are associated with each data type. Some are quite creative.
This summary sheet provides an overview of common charting options at your disposal. Again, note which charts are associated with each data type.
This video discusses how to construct various charts in Google Sheets, a free software package that can be used for data visualization. Recall the different types of data we discussed in Unit 2. As you watch, try to identify the type of each data point. Also, note which charts are used with each type of data.
This video provides an example of a simple visualization. Watch as a data scientist turns a sketch and numbers from a notebook into a digital visualization, and note how much quicker the graphic conveys the information than the raw data.
Engineers are guided by the KISS principle: Keep It Simple, Silly! This video gives tips for ensuring that your charts are understandable and to the point when you include them in a presentation.
Data visualizations should help you tell a story. Watch how this data scientist makes his charts interactive and engaging. By doing so, he involves the audience with the message and reaches them in a more memorable way.
Whether on purpose or not, a statistician can mislead an audience with a chart. This article explains some chart design principles and common mistakes novice data analysts make. Think about the statistical charts you have seen on billboards, in the news, and in research studies. Using these principles as a guide, would you classify any of those charts as misleading? Be sure to take note of the suggestions for successful dashboards.
Take this assessment to see how well you understood this unit.