Welcome to my portfolio, where I showcase my work in data science, machine learning, and software architecture. Curious about a project or want to collaborate? Reach out anytime at jwatterson99@proton.me — I'd love to connect.
Explore more:
🌐 Website: jwatterson.com
💻 GitHub: j-watterson
🔗 LinkedIn: jw-data
Data Analyst/Engineer with 4+ years of experience in data analysis, engineering, and business visualization. Skilled in building and optimizing Extract, Transform, Load (ETL) pipelines, managing data warehouses, and automating workflows. Proficient in Python and SQL, with a strong emphasis on operational efficiency and data-driven insights. Currently pursuing an M.S. in Data Science.
The COVID-19 Local and Global Analysis conducted in R provided comprehensive insights into the spread and impact of the virus at both community and international levels. The study utilized advanced statistical techniques in R to track infection rates, identify hotspots, and evaluate the effectiveness of public health interventions worldwide.
Stellar Study Cards began as a personal solution to a common problem—I was spending too much time handwriting index cards. To streamline my own study process, I built the platform from the ground up using HTML, CSS, and JavaScript. What started as a simple tool quickly evolved into a fully functional, user-friendly website designed to help anyone study smarter and more efficiently. Now, it’s a resource built not just for me, but for everyone who wants a faster, cleaner way to master new material.
In this project, I explored the power of BERT—a state-of-the-art transformer-based language model—for sentiment classification. I walked through the full pipeline: from preprocessing text data to loading a pretrained BERT model and fine-tuning it for high-accuracy sentiment prediction. Designed for learners with intermediate Python experience, the project leveraged tools like NumPy, Pandas, and PyTorch, all within a cloud-based environment to support hands-on experimentation. My goal was to make advanced NLP more accessible by focusing on practical implementation and real-world application.