Inspiration
We want to analyze student performance metrics from grade 3 to grade 8 with student features and use it to understand education quality in NYC from 2013 to 2017 for parents, government, and educational sectors to refer.
What it does
It gives the distribution of student performances, the trend of student performances over year and grade, and the percentage of students in a certain test result level given a feature (ethnicity, economic status, gender, ELL, SWD).
How we built it
We employed Python. Specifically data cleaning and visualizations.
Challenges we ran into
We put some effort in transforming dataset shape in an effective way.
Accomplishments that we're proud of
Clear and concise visualizations.
What we learned
It is beneficial to create a draft of logic before writing code.
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