Recent masters graduate passionate about data science with experience working in both team-based and independent capacities, brings a strong work ethic and excellent organizational skills to any setting and who looks forward to see many opportunities challenges in the data science field.
Epidemiological Methods; Weapons Ownership vs. Mental Health, Fall 2020
Bayesian Statistics; 2020 NFL Predictions, Summer 2020
Bayesian Statistics; 2020 Democratic Primary Nomination, Winter 2020
Survey Sampling; Workplace Diversity, Fall 2019
Advanced R Programming; Fall 2019
Applied Statistical Learning; Spring 2019
Used different machine learning methods such as linear regression, cross-validation and random forests to determine housing prices.
Data Management in SAS; Spring 2019.
Assessed how alcohol consumption and sleep disorders contribute to depression using SAS.
Categorical Data Analysis; "Predicting Wine Quality", Fall 2018.
Applied algorithmic variable selection methods such as regression trees, random forests, pruning, and bagging to determine the predictors that affect wine quality and their overall effectiveness.
Tested different methods for handling missing data. (R)
Linear Regression; Car Values; "How Much is Your Car Worth", Spring 2018.
Used data collected from Kelly Blue Book for 2004 used General Motors (GM) cars in order to develop a regression model to determine car values based on the mileage, make, model, engine size, interior style, and cruise control. (R/SAS)
Applied Linear Algebra; "Urban Population Dynamics", Fall 2017.
Developed and designed a population model using linear algebra methods and the R programming language.
Studied the population dynamics of Los Angeles for the purpose of making a planning proposal to the city which will form the basis for predicting school, transportation, housing, water, and electrical needs for the years from 2000 onwards and determined the stability of human population in the long run
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