Core Technologies: Python, MySQL, Spark, Tableau, Pandas, NumPy, Matplotlib, Seaborn, SciKitLearn, Anaconda, Jupyter Notebooks, Git/GitHub
Core Competencies: Data Storytelling, Applied Statistics, Machine Learning, Natural Language Processing, Classification, Regression, Clustering, Time Series Analysis, Anomaly Detection
Primary Focus: Data Analytics
Additional Skills: Microsoft SQL • Python • Tableau
I’m a results-driven data scientist and United States Air Force Veteran with a strong background in military leadership and operations. I’m adept at analyzing complex data sets, identifying trends, utilizing statistical analysis tools, and providing actionable insights. I’m also skilled in leading teams, setting strategic priorities, and effectively communicating findings to senior leaders. Finally, I’m committed to delivering high-quality results and driving data-based decision-making in order to make organizations better.
My Capstone Project: Project Apollo – Blue Chip Predict
The world is unpredictable, but it is increasingly connected. Using 80 quarters (20 years) of publicly available data from 38 different economic, social, political, and environmental measures, our team built multiple regression models to predict the next quarter’s revenue for three blue-chip companies: Starbucks, Ford, and AT&T. LassoLars was the best performing model for Starbucks, and the Tensorflow Neural Network model was the best for Ford and AT&T. All models beat the baseline. My largest contribution was the presentation slides where I took all our interesting insights and model results and distilled them into a 10-minute presentation to deliver the results of our project. I also contributed heavily to the data wrangling and exploration phases, notably having the insight to adjust our target for inflation and to shift that column so we would be using the previous quarter’s data to predict the next quarter’s revenue.
