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
Weekly Challenge #219
Who said girls can't do math? It's 2020 and girls are thriving in many math and science areas, including data science. Just check out our Women of Analytics page!
In this week's challenge, we have the results from the New York City Public School standardized math exams from 2013 to 2019. Examining only 8th grade students, find the percentage of female students that achieved a Level 4 score for each district in the years 2013 and 2019. Then rank the districts according to the biggest improvement in this percentage.
Data Source: https://infohub.nyced.org/reports/academics/test-results
C:\ProgramData\Alteryx\Engine\Engine_24776_895df09a9214472db942422f50a7e92c_\Engine_27360_5e4cbac7b52f401abeef0173e4360a06_.yxdb
Single
Profile
district-math-results-2013-2019-(public).xlsx|||`Gender$`
False
1
district-math-results-2013-2019-(public).xlsx
Query=`Gender$`
21
25.45
20
17.89
30
17.74
[Grade] = "8" AND [Category] = 'Female' AND ([Year] = 2013 OR [Year] = 2019)
Custom
[Grade] = "8" AND [Category] = 'Female' AND ([Year] = 2013 OR [Year] = 2019)
2019-2013 - Descending
First
3
First 3
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challenge_219_TonyA