Learners will analyze and interpret a box plot and evaluate the spread of the data. Learners will compare it with a different visualization of the data to see how the two compare, discuss the limitations of the two types of data displays and formulate questions.

Compare a histogram and map to determine the differences in the information conveyed in each data display.

Interpret a scatter plot to find patterns in the number of tropical cyclones from 1842 to 2018.

In this mini-lesson, students analyze soil moisture quantities associated with Hurricane Harvey around Houston, Texas on August 25, 2017.

Students interpret a double bar/column chart comparing the number of tropical cyclones in different locations.

Interpret the map, or model, to find patterns in the occurrence of tropical cyclones from 1842 through 2018.

This video provides tips for teachers on helping students make sense of data to help them understand and work with data. It is based on the work of Kristin Hunter-Thomson of Dataspire.org and uses data from the My NASA Data Earth System Data Explorer.

The Earth System Satellite Images, along with the Data Literacy Cubes, helps the learner identify patterns in a specific image.  

Data scientists work with data captured by scientific instruments or generated by a simulator, as well as data that is processed by software and stored in computer systems. They work with scientists to analyze databases and files using data management techniques and statistics. From changes in sea level,  atmospheric composition, or land use, data scientists help make sense of the petabytes of data that NASA collects and stores.

At the core of scientific visualization is the representation of data graphically - through images, animations, and videos - to improve understanding and develop insight. Data visualizers develop data-driven images, maps, and visualizations from information collected by Earth-observing satellites, airborne missions, and ground measurements. Visualizations allow us to explore data, phenomena and behavior; they are particularly effective for showing large scales of time and space, and "invisible" processes (e.g. flows of energy and matter) as integral parts of the models.