M.S. Data Science.
Using a dataset of historical option pricing and relevant data, my goal is to create a tool that can be used to analyze current market conditions for a stock and recommend an ideal options trading strategy using a regression model to forecast trade profitability.
Using publicly available data, I'm attempting to build a model that can create optimized daily fantasy sports lineups. The goal, which is an uphill battle is to create a pipeline that can be used to deliver consistent, profitable results.
Looking at anonymized retail sales information to learn about the impact of holidays and promotional events on weekly retail sales, as well as building a regression model to predict weekly sales moving forward.
Taking the perspective of an airline, I looked at air travel statistics. The goal was to analyze the safety of commercial air travel and identify ways that airline safety may appear to be a riskier proposition than it truly is.
Using data provided by Touring Plans, I looked at the wait times of the Walt Disney World ride Splash Mountain, looked at the factors that may influence wait times and built a regression model to predict future wait times.
The concept of technical analysis is a divisive one among traders. I took a look at the simple moving average crossover strategy, tested the performance of various parameters across a large number of stock tickers and built a classification model to generate Buy and Sell signals.
For use on other projects, I put together a Python API wrapper for AlphaVantage, a very useful website to retrieve a large amount of historical and real-time stock market data.
What makes a Formula One race interesting? Are there factors that decision makers could look at to improve the overall quality of racing? Using fan reviews of races, combined with data involving the races, I attempted to answer these questions.
A small command line-based weather and weather forecasting application written in Python using the Open WeatherMap API
Looking at a dataset of films along with their box office performance, I attempted to analyze what factors make meaningful contributions to box office revenue.
Responsible for the handling of daily and hourly reporting tasks for Walt Disney World and Disneyland Resorts.
Handle daily data-quality tasks including data cleaning.
Develop new reports using SAP Business Objects and SQL to provide greater information and insights to decision-makers.
Worked with new cast members to ensure they were performing within the standards and values synonymous with the Disney brand.
Developed new attraction-specific tools to improve operational efficiency.
Managed staffing on a daily basis to ensure operations were properly staffed but also within budgetary goals.
Used VBA to develop an automated system for cast recognition, significantly reducing the time needed to generate recognition certificates.
Developed a tool using Python to better track historical operational data. Reports were automatically generated and available to decision makers to gain a better understanding of how our operations were trending over longer-time frames. This allowed for potential issues to be resolved before they had a significant impact on the operation.
Supporting software used by NASCAR teams during races. Ensuring data quality is as accurate as possible.
Overseeing the proper startup and shutdown of software used during races.
Providing testing and feedback for staging versions of software before upgrades are moved into the production environment.
Trading stock market derivatives primarily using short-selling strategies.
Developed a system to store historical data into a PostgreSQL database for further analysis.
Constantly exploring historical data to uncover patterns and find advantages.