Claire Casey

Statistics graduate familiar with gathering, cleaning, organizing, and analyzing data for use by technical and non-technical personnel. Advanced understanding of statistical, algebraic, and other analytical techniques with a strong drive to expand experience and technical skills. Highly organized, motivated, and diligent with significant background in R and Python programming languages.

Data Visualization, Spring 2023

• Implemented and developed a deep understanding of data storage and visualization best practices
• Conducted comprehensive analysis and data cleaning of sizable datasets using R and the versatile tidyverse suite, facilitating data-driven insights to determine optimal visualization strategies
• Generated static and interactive data visualizations t0 effectively communicate discoveries from data analysis in R Studio using plotly/ggplot2, R shiny, and Mapbox packages

Statistical Consulting, Fall 2022 - Spring 2023 

• Advised clients made up of UMass Amherst graduate students and professors from a variety of fields on statistical and data analysis decisions
• Communicated high-level technical ideas effectively in order to meet clients' expectations
• Worked with peers to develop a test statistic and randomization test for a client using Massachusetts traffic data to prove the presence of bias in 2 traffic violation cases

Master's Project: Flight Delay Prediction, Fall 2022

• Built, analyzed, and compared the predictive ability of a variety of binary response statistical models in R pertaining to 2021 Reagan Airport flight data,
• Fine-tuned a final GLM logistic regression model that showed a 3% improvement in predictive ability of flight delay from starting model
• Collaborated with a small group to compose and present weekly reports of statistical findings alongside appropriate visualizations to peers

Statistical Computing, Fall 2021

• Ascertained skills to design and carry out an end-to-end statistical analysis using pandas, NumPy, scikit-learn, PyTorch libraries in Python
• Collaborated effectively with a small group to develop solutions to real-world problems using technical and theoretical statistical methods
• Developed and analyzed regression and clustering models for movie recommendation prediction using Python, improving model performance by 76.3% through the use of Random Forest clustering