For over 15 years, I have specialized in evaluating business risk and forecasting growth across various sectors, including finance, education, and healthcare. At UCLA Health, I work as a data scientist, focusing on predictive modeling to enhance clinical decision-making, improve patient outcomes, and reduce mortality rates.
In addition, I serve as an adjunct professor at several universities, where I bridge the gap between academic theory and real-world analytics.
M.S. Applied Data Science
University of San Diego, Shiley-Marcos School of Engineering
Executive Certificate Programs
Cornell University
B.A. Economics
University of California, Los Angeles (UCLA)
In this this hands-on, exercise-driven course, students will learn the role of data science in decision-making, basic statistics, entry-level Python programming, and data analysis, using tools such as Excel, SPSS, and Python. Designed for absolute beginners, business analysts, and non-technical managers, the curriculum includes problem-solving exercises for assignments during and outside of the class.
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An examination of the role of data science within business and society. The goal is to identify a problem, collect and analyze data, select the most appropriate analytical methodology based on the context of the business problem, build a model, and understand the feedback after model deployment. Emphasis is on the process of acquiring, cleaning, exploring, analyzing, and communicating data obtained from variety of sources. Assignments require working with data in programming languages such as Python, wrangling data programmatically and preparing data for analysis, using libraries like NumPy and Pandas.
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This course is an introduction to relational database management systems. You will use a relational database management system to create, maintain, and secure a database in order to analyze data. You will create filters, sorts, queries, forms, and reports. The course emphasizes the skills you need to meet user requirements.
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This signature program has been designed to help UCLA undergraduate students practice their team building, public speaking, and presentation skills – all vital for their professional development.
Guest Lecturer (Volunteer):
Panel Judge (Volunteer):
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worked with Litewallet software development, design, support, and marketing teams.
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In this 2-hour project-based course, you will learn how to import data into Pandas, create embeddings with SentenceTransformers, and build a retrieval augmented generation (RAG) system with your data, Qdrant, and an LLM like Llamafile or OpenAI. This hands-on course will teach you to build an end-to-end RAG system with your own data using open source tools for a powerful generative AI application.
Valid at the following institutions:
An interactive virtual workshop series to develop data science skills, advance career, and drive social change. Presented by University Of San Diego Applied Data Science Master's Program and San Diego Tech Hub
Certificates in: