Grant Nolasco Email & Phone Number

Los Angeles, Ca, Us

Los Angeles, Ca, Us

San Diego, California, Estados Unidos

- Developed methods like Bayesian inference to estimate hourly visitation to point of interests especially to locations with little information- Used numerous machine learning methods and statistical analysis to help clean fraudulent mobile location data from our ecosystem- Improved previous algorithm that predicted the number of people seen in a location using mobile devices by controlling the impact of device data fluctuations, adding in additional features, and improving the model to give better results using PySpark and SQL

Pasadena, California

- Presented meaningful insights and recommendations to clients using mobile location data to answer their questions- Wrote Python scripts to create customized analyses on large datasets for numerous clients or R&D purposes- Managed several internal R&D projects to improve current products and developed new ones like determining the best combination of parameters to screen out potential outliers from our data ecosystem- Visualized findings through numerous data visualization softwares like Tableau, Carto, and R and packaged those findings using Microsoft Powerpoint or Tableau dashboard for clients and other departments

- Wrangled research data into formats more appropriate for analysis using R- Created visualizations using ggplot and used methods like word association for exploratory analysis- Developed methods to determine how successful a working group is based on their reports using NLP- Analyzed data given to me using R to answer questions that working group has for NCEAS- Automated certain tasks for NCEAS and its working groups using Python

Santa Barbara, California Area

- Performed k-medians/k-means clustering to create a customer segmentation model to provide fundraising support - Worked on an interactive map for YMCA website using Javascript, HTML, and CSS- Imported and exported data from the Raiser's Edge database

- Organized auto insurance claim adjuster notes into useful data for actuarial analyses and found useful variables that will improve the structured model to predict the severity of bodily injury claims- Used Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) to find the structure and topics of the given notes- Predicted the severity of such claims using SVM, Naive Bayes, Random Forest, Logistic Regression, and Gradient Boosting

- Performed statistical analysis (i.e hypothesis testing) and logistic regression on the framing of UCSB Catchers to determine what areas the coaches should develop for the upcoming season- Created a web scraper using Python to compile data from numerous websites to find compelling statistics that provides insight towards what wins games in the Big West Conference- Determined the statistical performance drop-off for each of the UCSB starting pitchers in 2018 as they progressed through the opponents order multiple times using 2017 data- Created visualizations for the Director using ggplot- Discussed my findings and reports to the Director of Analytics of UCSB Baseball

- Performed manual regression tests using a spreadsheet of test cases to verify any bugs found in the upcoming update or hotfix- Assisted the EVP of Operations and Technology with data analysis projects using SQL- Wrote and performed automated test cases using Python to detect any bugs- Communicated with tech developers and engineers about upcoming features to develop new test cases for regression/feature testing- Documented and recorded problems and bugs to the tech department using JIRA- Participated in sprint meetings with tech and other departments to provide feedback and discuss the statuses of our tasks, goals, and upcoming deadlines

Master Of Science - Ms, Statistics, 3.93

Bachelor Of Science - Mathematical Science/Statistical Science, Data Science, 3.54