
A very talented data analyst who is proficient in several different analytical technologies, such as Python, Tableau, Power BI, and SQL. I have a master's degree in informatics and an excellent academic record. I have worked on various noteworthy projects, like predicting changes in US housing prices and streamlining inventory management at the university's Center for Advanced Computer Studies. My work as a business development analyst and database research assistant/analyst has sharpened my analytical skills and made a big difference in operational efficiency and data quality. I am an invaluable contributor to any data-driven firm because of my great communication skills, commitment to quality, and enthusiasm for using data to make informed choices.
User Interface / User Experience: Engaged with users to understand their needs and preferences. Created task flows and personas. Developed sketches, scenarios, and design options. Designed prototypes and conducted usability tests. Focused on improving inventory management at the University of Louisiana at Lafayette's Center for Advanced Computer Studies (CACS).
Alzheimer’s Analysis using R: Developed a robust and reliable model using R to accurately estimate the primary cause of Alzheimer's/Dementia among individuals aged 60 to 96. Ensured the model's trustworthiness by employing rigorous data analysis techniques and adhering to best practices. Leveraged advanced statistical methodologies to uncover key factors contributing to the condition, enabling accurate predictions and actionable insights.
Forecasting Changes in House Prices in the United States: Conducted comprehensive analysis of real estate transactions and house price data from multiple reliable sources. Utilized this data to develop a predictive model for forecasting future home prices. Employed advanced techniques, including merging and training data using a robust random forest model, to accurately predict price trends. Implemented rigorous back-testing methodologies to quantify errors and validate the model's performance before incorporating additional predictors.
Unlocking the Mysteries of Insurance Costs: Focused on forecasting health insurance costs for optimal premium adjustments. Analyzed historical medical data to predict individual coverage expenses, leveraging key factors like BMI and smoking habits. Employed diverse graphical tools (bar graphs, plots, heatmaps) for insightful dataset exploration. Implemented various regression techniques, best subset, ridge, and lasso, to enhance cost estimation accuracy. Utilized K-fold cross-validation and validation set methods to ensure robust model performance. Facilitated data-driven decision-making for insurance premium setting, highlighting the impactful role of BMI and smoking behaviors.