Ezra Aguimatang
- Data Scientist
- San Diego, CA, USA
Personal Summary
University of California Santa Barbara (UCSB) new grad with a B.S. in Data Science and Statistics as of March 2023. Experienced in Machine Learning, Statistical Analysis, and Database Manipulation and is proficient in Python, R, SQL, MatLab, and Base SAS. Known to be enthusiastically curious about the role of Data Science and Software Engineering in understanding human interactions and the natural universe. Is open to new opportunities and is interested in full-time and part-time positions in Machine Learning, Data Science, Data Engineering or Software Engineering.
Former swimmer of 7 years for South Bay Aquatics (SBA), with Age Group coaching experience for Santa Barbara Swim Club (SBSC) and SOL Swim Team (SOL).
Work Experience
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Developed and instructed Data Carpentry Workshops on Machine Learning, Git Version Control, Geospatial Raster Data Analysis, etc. in R and Python for Undergrad, Grad, Ph.D. Candidates with intersectional fields of study.
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Researched for various projects with Senior Data Scientists and Professors (ex. Raspberry Pi Coding in MicroPython).
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Architected the process to develop a Python Machine Learning Regression Model utilizing manually collected data from reliable sources to predict the likelihood of success for a user based on weather/water conditions, etc.
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Constructed large datasets with Python’s Pandas Module: identifying feature variables, cleaning over 10000 data observations; utilized Python’s Seaborn to visualize and report statistical findings to Senior Software Engineers.
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Collaborated with Doctors, Ph.D. Candidates, Grad Students, and experts in the field of Data Science and Ethnic Studies to publish research on 21st Century Skills in an effort to evaluate modern Teaching Pedagogies.
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Implemented statistical analysis and R programming to construct visual data representations to report to Grad Students.
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Assisted in teaching undergrads statistical theory and R programming, and developed an understanding of the process behind conducting Graduate level research on unfamiliar topics through manual data collection/data cleaning.