As in the previous lesson, the goal of this lesson is for students to be able to create a range of visualizations to analyze complex sets of large data and to meaningfully interpret the patterns they uncover. Students use statistics to deepen the meaning of knowledge gained by visualization. The hooks are again conclusions they can draw about themselves from relevant data, including various geographic perspectives on their life and facial recognition of their own features. The lesson uses Excel as well as Python to manipulate and visualize data. Students examine multidimensional data sets using scatter plot arrays and view geographic and social data using heat maps and directed graphs. Students experiment with object recognition and face recognition. They are challenged to discover clustering and linear correlation patterns lurking in data sets distributed across student computers and school sites, such that data cleaning and warehousing are necessary. Finally, student teams choose a question and answer it using large data. Essential Questions
|  |
|
|