TATUM GOOD Data Scientist • Analyst • From Patterns to Clarity

My Story

Professional Me

I’m Tatum Good, a data scientist passionate about turning patterns into clarity. I have recently completed a BS/MS in Data Science at Willamette University, with minors in Computer Science and Mathematics. My work blends strong technical foundations in machine learning, statistical analysis, and data visualization with a deep interest in real-time data and its power to inform decision-making. My entry point into data science came through visual learning—seeing how models and algorithms could tell stories and surface insights that might otherwise stay hidden. That sense of discovery continues to drive me. Whether I'm building dashboards, cleaning messy data, or presenting findings, my focus is always on impact: making data interpretable, usable, and meaningful. I’ve led and contributed to diverse data projects, from analyzing state-level income and education trends to visualizing patterns in news articles using natural language processing. Across all of it, I aim to build tools and models that not only work—but communicate. As a student-athlete, I bring discipline, time management, and teamwork to every project. I thrive in collaborative environments and am eager to contribute to organizations tackling real-world challenges with data.

Python Programming

My preferred Python-based toolkit to use for data analysis, machine learning, scientific computing, and research.

R Programming

Tidyverse dplyr tidyr ggplot2 shiny

A comprehensive toolkit for data exploration, visualization, and reporting in R.

Data Infrastructure

duckdb MinIO

A stack for orchestrating data pipelines with modern databases, cloud platforms, and object storage.

Data Analysis and Visualization

Colab

My go-to tools for development, version control, reproducibility, and environment management for all data analysis and visualization.

Skills: Python, Matplotlib, Scikit-learn, Pandas, NumPy, Anaconda, Pytest, Jupyter, R, RStudio, Plotly, Tidyverse, dplyr, tidyr, ggplot2, Shiny, PostgreSQL, MongoDB, Apache Airflow, Supabase, Google Cloud Platform, Grafana, DuckDB, Railway, MinIO, Visual Studio Code, GitHub, Docker, GitLab, Google Colab

Featured Projects

Personal Website

The Odds of Participation

   

This capstone examines how education and income levels relate to sports betting participation across U.S. states. Using data from the U.S. Census and state-level sports betting reports (2018–2023), the study explores whether modern app-based gambling follows traditional socioeconomic patterns. Built with Docker and PostgreSQL, the pipeline supported statistical modeling and visualization through linear regression, random forests, and choropleth mapping.

Check it out!
Personal Website

New York Times Data Visualization

 

This graduate research project analyzes 173 New York Times graphics from 2015–2024 to explore how data visualization practices changed during the COVID-19 pandemic. Using qualitative coding and R-based analysis, the study examines shifts in graphic design, complexity, and communication themes to understand how visuals both reflected and influenced public perception during the crisis.

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Metro

Metro Pipeline

     

This project delivers an end-to-end data pipeline for analyzing public transportation ridership and fare data. The system ingests, processes, and visualizes daily rider behavior and fare usage trends using Airflow, PostgreSQL, and Grafana. Our goal was to support data-driven decisions around fare policy, rider equity, and system performance.

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Student Academic Factors

Student Performance Factors

   

As team leader, I guided the group through a project analyzing factors that influence student exam performance using a synthetic dataset of over 6,600 records. The work involved exploratory data analysis and the development of predictive models to investigate relationships between sleep, study habits, motivation, and socioeconomic status. Feature engineering and data visualizations were used to identify traits associated with an “ideal” student profile.

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