PRDV200: Communicating with Data

This video introduces the concepts you will learn in this course. Pay attention to the examples of data presentation in the video. What types of data are presented? How can we present data in a way that brings clarity to the audience? How can we solve information problems? As you watch the video, answer these questions and take notes.

This lecture discusses how color, size, and whitespace can increase the amount of information captured in a single graphic and how the deliberate use of design principles can reduce the "time to insight": the time it takes the audience to unpack and understand information. As you watch the video, be sure to take note of these principles.

For a data visualization to be considered useful, it must be understood when presented. Watch this summary of how visual variable properties can guide the creation and presentation of data visualization.

This video will further explain how your audience perceives and understands the visual information you display. Note how the speaker uses the visualization principles discussed to create the figures used in the presentation. The speaker also shares tips for thinking systematically when constructing data visualizations. Be sure to take notes as you watch this presentation.

Watch the video to see how to measure dependent variables and different types of variables. In an experiment or study, a dependent variable is the condition or concept being studied. A control variable is a condition that remains the same throughout the study. How can we classify measurements of variables? In this video, you will cover four basic scales of measurement. Pay attention to the definition of each scale, examples, and how or why the scale is used. Think of examples from your own experiences to make a connection to each scale and its use.

When a data set is missing data, full of mistakes, or in a rough form, data scientists call it "dirty data". Data analysis software will sometimes return an error and not analyze dirty data. In other cases, the software will run an analysis, but the dirty data will bias results. This video will show you how data can become dirty and some ways to clean it. Think about data you have worked with in the past. What errors were present in the data sets?

Big data, "data-driven", and other buzzwords are everywhere in modern industry. What do these terms really mean? These five videos define these terms and show how they relate to data visualization. You may want to take notes on these terms.

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.

All statistical analyses are guided by a research question. Properly defining all variables and clearly stating a problem are the first steps in the data presentation process. This article discusses the operationalizing of variables.

A visualization must be easily understandable by everyone who sees it. This article demonstrates some ways to ensure that charts are understandable and engaging.

Review these "dos and don'ts" of presenting data in infographics. Pay attention to the amount of information, images, colors, and charts you should use. Take notes and become familiar with how to conduct a squint test before finalizing your presentation.

This article gives an overview of dashboards in visualization and data storytelling. The authors provide five simple principles for creating simple and effective dashboards. Note the five principles the authors provide.

Read this article on using figures and tables to simplify or break down complicated information in a way that is accessible and understandable to your audience. Take notes on each section and pay attention to the type of figure/chart used, what it requires, why it is used, when it is best used, and what to avoid when using it. Think of a data set where you can apply each type of figure/table to present.

Read the article and take notes on preparing a powerful slide deck. Use a presentation you prepared or located on a topic of interest to make this activity more engaging. Follow the ten tips, adjust the slides, then rename the presentation and save it as the new version. Once you are done, compare the two versions and reflect on the improvements.