Hi! I am a former Teacher and Cook turned Data Scientist/Analyst living in NYC by way of California. I am interested in ways data can be used to further goals in social justice, education, and language. I seek to make data digestible to audiences of various degrees of data literacy in the same ways that a good teacher can break down a difficult concept. In my off time I love biking, playing music, cooking, and hiking with my dog. Take a look below for samples of my work and enjoy!
About Me
Hi! I am a former Teacher and Cook turned Data Scientist/Analyst living in NYC by way of California. I am interested in ways data can be used to further goals in social justice, education, and language. I seek to make data digestible to audiences of various degrees of data literacy in the same ways that a good teacher can break down a difficult concept. In my off time I love biking, playing music, cooking, and hiking with my dog. Take a look below for samples of my work and enjoy!
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Below you will find a couple quick summaries along with interactive graphs of projects I have worked on. For a more detailed look, along with other projects, click on "PROJECTS" at the top of the screen. ________________
SEATTLE POLICE DEPARTMENT TERRY STOPS: This project seeks to investigate bias in Terry Stop data from the Seattle Police Department's Open Data government program from the years 2015 to 2019. Terry Stops (Stop and Frisk) derive their origin from the Supreme Court case Terry v. Ohio in which a police officer stopped John Terry, along with two other men, and found two firearms on the men. The Supreme Court ruled that using "probable cause", an officer can stop and frisk a civilian on the street. Using data for 28,530 stops, we can see that although racial groups are arrested at roughly the same rate, Blacks are stopped at more than 4 times their population percentage while American Indian/Alaskan Natives are stopped at more than 5 times their population percentage. I also found that the majority of stops and arrests are focused around areas in Seattle associated with high displacement risk according to a study conducted by the Department of Planning & Development.
K-8 SCHOOL COMMENTS - SENTIMENT ANALYSIS/TOPIC MODELING: In the public education debate, we often hear from official voices whereas the average parent or student are often left out of the loop. Although school surveys are meant to capture this, often their questions are carefully crafted in ways that can feel very formal, and research has shown that people tend to rate things more favorably than they may truly feel. This project parses 22,662 user comments, regarding individual NYC K-8 public schools, to investigate topics of concern and to generate a sentiment score as a measure of community satisfaction with a school. Through the topic modeling process along with sentiment analysis, concerns about testing and programming/services appeared throughout. These are important issues that should clearly be communicated to parents through the school and DOE. It is a red flag that parents are expressing concern though an informal channel for such formal services such as dual language programs or special needs accommodations which if not given could imply legal ramifications.
