I am a seasoned Data Scientist with a strong work ethic and 6+ years of professional analytic experience. I am skilled in statistical methods, analytical techniques and software. I have a deep understanding of several statistical models. And a proven track record of organizing and managing a multitude of difficult projects simultaneously and delivering them complete and on time.
I enjoy process optimization. I like automating and simplyfying procedures, making them more efficient and less error-prone. I absolutely love data visualization! It combines my information summarization knowledge and art background. Anyone who knows me knows that organization is my strongest skill (both professionally and personally).
I have built data pipelines that extracted, transformed, cleaned, scored, harmonized, and stored data from 80+ different sources. Data from countries all over the world, translated in multiple spoken languages
Made many dashboards in Tableau, R, and PowerBI.
Co-authored multiple academic publications
Built websites, the user interfaces (PHP/HTML), tables and models behind them (SQL). I enjoy teaching others and have made R interactive tutorials and YouTube tutorials. Volunteer to train onboarding colleagues. I created my own internships, my first project led to a job offer.
Designed experiments from a statistical perspective & conducted analysis of experimental data. Survey data, categorical, and quantitative research.
Made many predictive models in a variety of fields: biology, finance, real estate, baseball, public health, but most of them in health and medical data (my personal favorite).
I have worked on projects in marine biology, human behavior, social media data, cancer research, genetic psychiatry research, cancer diagnostics.
Now, I focus on process improvement solutions, building tools that assess the quality of medical devices, provide leadership with data driven solutions, and help make people's goals easier to complete
Technical: R, SQL, SAS, MS Access, PowerBI (DAX & M), Tableau, Advanced MS Excel, Visual Basic, SPSS, Java, HTML, PHP
San Diego State University
Relevant Coursework: Statistical Communication, Predictive Analytics, Computational Database Fundamentals, Statistical Consulting, Machine Learning, Multivariate Statistics, Advanced Mathematical Statistics, Advanced Biostatistical Methods
San Diego State University
Relevant Coursework: Applied Regression Analysis, Data Analysis and Statistical Inference, Intermediate Computer Programming, Actuarial Modeling, Applied Multivariate Analysis, Probability and Statistics, Programming Languages, SAS programming, Data Management, Spatial Data Analysis, and Statistical Methods
BD Jun 2020 – Present
UC San Diego Health Sep 2017 – Jun 2020
UC San Diego Health Jun 2017 – Aug 2017
Trovagene Aug 2016 – Mar 2017
Center for Human Dynamics in the Mobile Age (HDMA) Oct 2015 – Aug 2016
Marine Conservation Ecology Lab, SDSU Aug 2015 – Dec 2015
Identified Most Influential Local Twitter Users. Excecuted data extraction and manipulation. Conducted clustering analysis and visualization. Presented of results to research center and partners.
Conducted Factor Analysis in census tract data in order to better understand socioeconomic patterns in San Diego County Cancer Patient dataset. Used knowledge gained from analysis to reduce the set of variables in the dataset.
Created a website using HTML, PHP and mySQL. Designed an ER model to store website data into the database. Maintained a connection to insert and update data from the website’s front page to the MySQL backend. Website would take in friend's birthday information (i.e. date, favorite restaurant, store).
Recorded a Series of YouTube Video in collaboration with fellow MS cohort mates. Gave a complete tutorial for Multiple Linear Regression with interactions that touched on the theoretical piece, exploratory data analysis, R code, diagnostics and applications.
Used R Markdown and the flexboard R package to create a dashboard summarizing psychological data from a local psychiatric Study.
Interactive tutorial for learning ggplot, made using the learnr R package
The goal of this analysis was to build a logistic regression model that was able to determine what variables are able to predict a prostate biopsy result. Being able to predict the results of a biopsy is of high importance as it reduces patient burden, patient surgery recovery time, and the probability of the cancer spreading upon biopsy retrieval.
The goal of this analysis was to build a model that was able to predict whether a patient will recover or die based on a set of physiological measurements taken upon patient admission. The analysis helped determine which physiological measurements were essential to patient survival. The hope is that this information will help physicians establish the most appropriate treatment for a patient and in turn increase their chance for survival.
Patients with prostate cancer have a great chance of survival when the tumor is located within the bounds of the prostatic capsule. Relative survival rates drop considerably when the tumor has penetrated the prostatic capsule and has spread through the body. In order to establish the most appropriate treatment for a patient, it is important for physicians to know whether the prostatic capsule has been penetrated. Using logistic regression, we built a model that is able to predict whether a patient’s tumor has penetrated the prostatic capsule with 0.84% accuracy and high sensitivity (0.842)
Built a regression model for predicting the house prices in various New York state counties using various economic and socioeconomic variables.
I created a Tableu dashboard and conducted Exploratory Data Analysis to explore Cancer patients in San Diego County. Presented Dashboard and results to lab's Director and other university professors. This simple project is very soecial to me as it lead to me being offered a paid position at the center (I was volunteering to get professional experience).
Used a variety of Non-Parametric tests (Wilcoxon rank sum test, Kruskal-Wallis rank sum test, Wilcoxon Pairwise Comparison test) to analyze data from a Trauma and PTSD Study
We conducted a retrospective study that analyzed data from a cohort of 286 women ages 20-79 who had previously been diagnosed with breast cancer and treated either with or without radiation therapy at the time of their first diagnosis. Chi squared tests were performed in order to determine if prior radiation treatment impacted the recurrence of cancer
We studied Distracted Driving Behavior Related to Cell Phone Use Among Uber/Lyft/Taxi drivers. The goal of this study was to address this gap in literature via the characterization of attitudes and behaviors toward distracted driving among Transportation Providers. In the process, we hoped to gain a better understanding of the demography involved and determine level of exposure risk to Distracted Driving and possible causes that are unique to this population.
Worked in collaboration eith the Marine Conservation Ecology Lab, SDSU. Our research interest was to measure effects of seagrass structural complexity on mesograzer diversity and community composition in San Diego Bay.
This project promoted the development of data science and information technology solutions for San Diego on important civic issues related to water drought and conservation, disaster response, and crime monitoring.
Theoretical presentation of Logistic Regression Modeling. It's applications, logistiic regression with interactions, & extensions