After three career fairs and the start of Reignite!, I am exploring more ways to use my data science skills. My first lessons in business intelligence are helping me connect business questions with the decisions a dashboard needs to support.

I turned a fictional zombie outbreak into a hands-on way to practice Monte Carlo simulation, Streamlit, testing, and data visualization. Zombie Carlo Simulator shows how a fun idea can still become a serious data science project.

Building What’s UP Outdoors pushed me beyond notebooks and into geospatial analysis, APIs, interactive mapping, Streamlit state, ETL, and application structure. This post looks at the technical problems I encountered and the Python tools I used to solve them. 

AI coding tools can accelerate development, but they can also make it easy to slip into vibe coding. Through What’s UP Outdoors, I am developing a workflow focused on learning and verification so I have enough understanding to call the project my own.

My first PyOhio introduced me to accessible Python talks, welcoming community spaces, and new professional connections. I left Cleveland with new ideas to explore and more confidence in my future as a Python developer.

This week, I stepped away from data science tools to strengthen a core Python skill: object-oriented programming. I applied what I learned by building a "complete" Blackjack game and discovered why planning object relationships early matters.  

This post reflects on volunteering with MCWT Camp Infinity, reconnecting with the classroom, and helping students explore the joy of coding through Scratch, Brython, and robots.

This post is a short update on my summer focus: preparing for the fall STEM career fair, strengthening my portfolio, and turning my current learning plan into clearer evidence of my data science skills.

This post reflects on my first independent Power BI dashboard project and what I learned while building it. Using a Great British Bake Off dataset, I practiced turning raw data into an interactive report while learning how much dashboards depend on cleaning, modeling, design, and revision.