Visualizing Data

Objectives

  • Students will learn how to make data visualizations using Python and Pandas.

Vocabulary

  • Pandas Dataframe n. a table with rows and columns
  • Data visualizations, n. Data visualizations are graphic representations of data and information.

Resources

Activity Steps

  1. Tell students that in this lesson, they’ll learn some basic data visualization tools, so that they can build their own narratives using data.
  2. Play the video titled, “Visualizing Data” from the beginning to the first 30 seconds. Here is the script for this section.
  3. First, discuss as a class your answers to the following question, “Why use data visualizations”
  4. Play the video titled, “Visualizing Data” from the 32 seconds - 7 minutes and 18 seconds. Here is the script for this section.
    • This section discusses the different types of graphs. Now that students have fully developed datasets in Google Sheets, they can analyze those datasets to find patterns and meaning. To start, review different types of data charts/visualizations, and what each one is useful for with students. For each of the visualizations below:
      • Introduce the structure of the data visualization
      • Define the type of data that should be used for each visualization

Remind students again about consent, and not adding additional information on other students that they have not given permission to be added, even if they may know something about the student themselves.

[TABLE]

  • Next, this video clip discusses how to define a function.
  • After, this section guides students through an example of how to use the predefined functions in the Data Activism textbook.
  1. Play video titled, “Video 8.3 DataActivismTextbook.mp4”. Here is the script that corresponds with the video.
    • This video describes how the Data Activism Textbook contains the syntax for Functions which use Pandas to aggregate the data. We created these functions to make it easier for beginners to learn how to analyze data without the complex syntax of Pandas. Once you access the Deepnote, in the Files section, you will see a file named, “functions.py”, which contains the functions students can use to clean their data and visualize it. Here is the “Functions of Intersectional Data Analysis” Deepnote link for the textbook.
  2. After students complete watching the videos, prompt students to make their own data visualizations. Here are the criteria: Create at least two data visualizations total, Clearly title the visualizations in order to indicate the data it represents, and Label your visualizations clearly to indicate the location of different data on your chart Also, The visualizations must be distinct from each other. This can be achieved in two ways: by presenting the same data using different chart types or by depicting different data columns from the dataset in separate charts. Also, They should demonstrate a pattern that students find interesting. Lastly, they will complete the data visualization activity on Deepnote.
  3. Instruct students to follow the steps in the Deepnote activity titled, “Data Visualization Student Version” to upload their group dataset and create their own data visualizations, which can be found on Google Classroom
    • Students will take the data they have from Google Sheets and move it over into Deepnote for analysis. To do this, they will download their sheet as a .csv file, after which they can import the file into Deepnote following the directions in their Deepnote lesson. Follow the steps below to get the dataset into a .csv file:
      • Open their dataset in Google Sheets.
      • Click on the tab you want to save as CSV.
      • Go to File > Download > Comma Separated Values (.csv). Using Google Sheets