Did you know that a child has roughly a 1 in 2.3 chance of being born into poverty? Or that air pollution contributes to at least 7 million deaths each year? These aren't just headlines; they're conclusions drawn from data. ​​

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Since eighth grade, I've been drawn to math and coding above all other subjects. Eventually, I found a clean intersection between the two: data science.  

 

Over time, my appreciation for data science only grew. I learned that numbers often speak louder than words, revealing patterns that words can misrepresent, overlook, or exaggerate.

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But I also discovered that such numbers are not always objective. In a world of flawed collection methods, systemic biases, and reputational face-saving, data can be twisted or concealed in similar manners. 

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As an aspiring data scientist with these values in mind, I have one overarching goal: to uncover meaningful patterns while considering the biases that might distort them, especially in the fields of education or health science.

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It is with these motivations that I created this blog and the projects that come with it. 

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