Walkthrough 4: Longitudinal analysis with federal students with disabilities data

This chapter explores cleaning, visualizing, and modeling aggregate data. Data scientists in education frequently work with public aggregate data when student level data is not available. By analyzing aggregate datasets, data scientists in education uncover context for other analyses. Using a freely available federal government dataset, this chapter compares the number of female and male students in special education over time in the United States. Analysis on this scale provides useful context for district and school level analysis. It encourages questions about the experiences of students in special education at the local level by offering a basis for comparison at a national level. Data science tools in this chapter include importing data, preparing data for analysis, visualizing data, and selecting plots for communicating results.