Hi, I’m Maggie Kuo! A current senior majoring in Integrative Neuroscience with a Digital and Data Studies minor. With an extensive background in research and an eye for problem-solving, I am passionate about exploring how technology and data influence human biases, perception, and decision-making. My interdisciplinary education allows me to approach problems with a blend of scientific rigor and data-driven analysis. I aim to drive meaningful change in how we deliver and manage care in an increasingly tech-driven world. Given the opportunity, I am eager to apply my skills to the data science field as an entry-level data specialist.
As I continue to finish my studies, I have gained valuable skills in Python conducting statistical analysis, data visualization, and analysis of pattern and trends. I plan on adding more skills to my toolkit by learning Tableu, SQL, HTML, and R by the end of the spring.
The purpose of this repository is to showcase my skills and the projects I have completed so far.
This section will be used to briefly describe the data analytics projects (in Python) done to solve stalkeholder cases.
Code: https://github.com/maggieigkuo/Portfolio/blob/main/Stop_and_Frisk.ipynb
Goal: Assess ways to decrease or, ideally, end racial disparities that occur during stop-and-frisk searches in New York City.
Description: The objective of this project was to analyze the 2023 NYCLU stop-and-frisk dataset. In a group of 3, we examined over 50 columns, narrowed them down to 18, and developed four research questions to identify any alarming trends or patterns occurring during stop-and-frisk incidents. The dataset required extensive cleaning, as there were many NaNs and null values across multiple columns. The analysis incorporated logistic regressions, mapping, and data visualization.
Skills: data cleaning, data analysis, regression analysis, data visualization
Technology: Python, Pandas, Numpy, Seaborn, Matplotlib, Statsmodels.api, GeoPandas
Results: From the analysis, Black male Americans in the Bronx and Brooklyn are more likely to be stopped and frisked, with many of the consensual cases leading to physical (verbal) escalations. The number of arrests increased when a supervisor checked an officer's logs, demonstrating a statistically significant relationship between arrests and supervisory oversight.
Code: https://github.com/maggieigkuo/Portfolio/blob/main/Social_Media_Data_Visualization.ipynb
Goal: Produce six visualizations from the data to identify key patterns and trends, effectively conveying a clear narrative about what the data reveals and its implications
Description: This dataset encompasses various age groups, genders, sexual orientations, and emotions across different social media platforms. The purpose of the project is to create visualizations that uncover significant correlations among these factors and provide meaningful insights into the results.
Skills: data analysis, data visualization
Technology: Python, Pandas, Numpy, Matplotlib
Results: Social media significantly impacts female users more than male and non-binary individuals. Platforms like Instagram have evolved into culturally powerful tools, capable of influencing a person's mood and shaping their self-image.
Binghamton University, State University of New York: Bachelor's degree, Integrative Neuroscience and Digital and Data Studies, 2021-2025
- Email: maggieigkuo@gmail.com
- LinkedIn: https://www.linkedin.com/in/maggiekuo/