Often as programmers we have a go-to language or library that we want to use for every problem. This is especially true of Data Analysts and Data Scientists whose backgrounds may not be in Computer Science. In fact, I'll be the first to admit I wish everything could be done …
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Let's face it: for many of us parents, the coronavirus (COVID-19) epidemic pandemic couldn't have come at a worse time. Not only do we have a legitimate fear of dying every time we step out of the house into a public place, we also have to face the reality that schools around the U.S. will be closed for over a month
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For the second half of this project, our goal is to understand how differences in school, borough and district relate to a school's mean SAT score and whether these differences actually have any meaningful correlation to the test scores. We will utilize several visualizations, some basic correlation analysis and hypothesis testing to help us discover trends and examine the significance of those trends.
This project takes a look at the 2012 SAT scores from New York City high schools, and explores insights and correlations found in related data. For ease of reading and to allow for a more in-depth discussion of the data cleaning processes, this project is split into two separate Jupyter notebooks: the first for data cleaning/preparation, and the second covering visualization and analysis.