EAS: Guided Project: Analyzing NYC High School Data

I finally finished writing up my guided project Analyzing NYC high school data. Among the things I found: a proxy for socioeconomic status, the percentage of students receiving subsidized school lunch had a strong inverse correlation with a school’s average SAT scores. The magnitude was greater than any other correlation, positive or negative. This also has implications for differences in SAT scores in relationship to the racial make-up of schools.

I appreciate any feedback, but particularly on my discussion of the analysis.

https://app.dataquest.io/m/217/guided-project%3A-analyzing-nyc-high-school-data/6/next-steps

Schools.ipynb (741.9 KB)

Thanks!

Click here to view the jupyter notebook file in a new tab

2 Likes

Hello @eas-sea,

Excellent job. You found a way to see all relevant correlations and do a very simple and clear job, I only have a few recommendations:

  • Your map could be improved greatly using folium or plotly.
  • I love how you deal to find outliers, and the trick to transverse the columns, I use plotly to visually see the outliers, but your trick is more neat.
  • You didn’t categorize using a function to further explore, it was not necessary but seeing all your good job maybe you would find even more.

I totally love your project: simple, with nice tricks, and found relevant information that helps to understand better the SAT scores predictors.