Hi, I’m Sarah Jane Fullerton

A passionate Data Scientist with hands-on experience in data analysis, machine learning, and data visualization. My work spans multiple industries, including healthcare, tech, and aviation, where I’ve built predictive models, developed dashboards, and utilized data to drive impactful decisions.

What I Do:

  • Data Science: Creating models to predict outcomes and uncover hidden insights
  • Data Engineering: Building pipelines and systems for efficient data flow
  • Visualization: Designing interactive dashboards and reports to help understand data

Whether you're looking for someone to help with your next data-driven project or you're curious about my work, feel free to explore my site.

Technical Skills

Category Skills
Programming Languages SQL, R, Python, Java, JavaScript
Big Data & Machine Learning PostgreSQL, BigQuery, Linux, Docker, Spark, Kafka, Hadoop, Data Modeling, Data Mining, Pytorch, TensorFlow, scikit-learn
Visualization Tableau, Power BI, Matplotlib, Plotly, D3.js, Bokeh, ggplot2
Other SQLite, HTML, CSS, Node.js, DevOps, Git, ArcGIS, Stata

My Favorite Projects

Time Series Analysis on U.S. Natural Disasters and Mental Health Disorders

GitHub: Time-Series

Nov 2024

  • Performing exploratory data analysis (EDA) to uncover trends, correlations, and patterns in mental health data and extreme weather events from 1953 to 2023.
  • Implementing time-series analysis to predict the impact of future weather events on mental illness rates.
  • Visualizing data to communicate findings, and using sentiment analysis to explore public emotional responses to natural disasters as a proxy for mental health outcomes.

Reddit Sentiment Analysis with API Web Scraper and ML Models

GitHub: Sentiment-Analysis

Nov 2024

  • Developed a Python-based API web scraper using PRAW to collect Reddit data on Hurricane Helene, followed by preprocessing and manual labeling of 200 samples.
  • Fine-tuned transformer models to automate sentiment labeling of the dataset, leveraging pandas for data manipulation and transformers for model training.
  • Used sentiment analysis to study how the public's emotional responses to the hurricane shifted over time, providing insights into community perceptions of natural disasters.

Spotify Music Trends Analysis: ML Approach to Understanding Global Shifts

GitHub: Spotify-Analysis

Nov 2023

  • Conducted a comprehensive analysis of Spotify's top 200 global dataset (2017-2023), exploring trends in audio features, genre evolution, and the impact of the COVID-19 pandemic on music preferences.
  • Applied clustering techniques such as spectral clustering to analyze genre patterns and investigated seasonal and collaborative trends in music.
  • Used random forest regression to explore predictors of song popularity, providing actionable insights for improving user experience and recommendations on the platform.

Experience

Download my resume here!

With a strong background in data science and analytics, I have gained hands-on experience through internships and contracts with a variety of organizations. From building predictive models to developing dashboards and maintaining databases, my work has driven strategic decision-making and operational improvements across different industries, including transportation, healthcare, and non-profit sectors. I am passionate about leveraging data to uncover insights and make data-driven decisions that positively impact business growth and efficiency.

Publications

Extremal Polynomial Norms of Graphs

Authors: Bouthat, L., Chávez, Á., Fullerton, S., LaFortune, M., Linarez, K., Liyanage, N., Son, J., and Ting, T.

Publish Date: Nov 8 2023. Read the full paper

Developed new norms on Hermitian matrices using complete homogeneous symmetric polynomials, enabling refined graph distinctions through eigenvalue-based analysis. Proved that CHS norms are minimized by paths and maximized by complete graphs (connected) and by stars (trees). Published findings accessible to a broad mathematical audience.

Exploring U.S. Natural Disasters and Psychological Distress: From Time Series Trends to Machine Learning Insights on Hurricane Helene

Authors: Fullerton, Sarah Jane.

Submission Date: Dec 1 2024. Read the full paper

Research paper submitted for my senior thesis at Claremont McKenna College. This research investigates the historical trends of psychological distress in the U.S. in relation to natural disaster occurrences. By analyzing long-term data, we examine how significant natural disasters relate to levels of psychological distress over time. The research employs Exploratory Data Analysis (EDA) and Time Series Analysis to identify patterns and trends between the frequency and intensity of natural disasters and the rise of psychological distress across various periods in U.S. history. Additionally, real-time data from Reddit was collected through a custom-built Reddit web scraper specialized for Hurricane Helene. This dataset was labeled for sentiment and used to train machine learning models for sentiment analysis, providing valuable tools for understanding emotional responses in real-time. Their adaptability makes them applicable for future use in crisis response. The findings of this research offer a dual perspective: understanding the broader historical relationship between natural dis- asters and psychological distress, and providing insights into emotional reactions to 2024 events.

Education

Claremont McKenna College - December 2024

Bachelor’s Degree in Data Science

  • Relevant Coursework: Applied Machine Learning, Remote Databases, Foundations of Data Science, Computer Science, and Software Development, Data Structures and Algorithms, Probability, Statistical Inference, Linear Algebra, Discrete Mathematics, Calc: 1, 2, 3

My other interests and hobbies include:

  • Contortion & Acrobatics - Flexibility and strength-building practices.
  • Writing - Crafting reflections, research, and creative projects.
  • Baking - Experimenting with recipes and sharing delicious treats.
  • Dancing - An outlet for energy and self-expression.
  • Embroidery - Check out my embroidery projects.

Get in touch

I would love to connect with you! Feel free to send me an email or find me on Linkedin :-).