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I went to university of Delaware’s Alfred learner college and received a Bachelor of Science in Applied Economics. I worked in finance for a 3 years at Morgan Stanley. Then decided to make a career change to data analysis and got a Certificate in Data Analysis & Visualization from University of Pennsylvania, The College of Liberal and Professional Studies. I am always looking for new projects to test and refine my coding skills. Some projects that I have completed are:

The purpose of this project was to see how over the course of 2 years how City Bike usage changed in the state of New York. A map was created of all the end bike stations and each of the stations is color coded to see how often that station was used in a specific year. Then the relationship between gender and bike usages was looked at for both years and displayed in a graph. Age was another factor that was looked. A bubble chat was great and a line graph was used to display the trends in age. Role: sole author Tools: Tableau Tableau public: https://public.tableau.com/views/2019vs2020citibike/Birthyear?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

This is a map of earth quakes across the world that have happened in a week. The information was pulled from https://earthquake.usgs.gov/earthquakes/feed/v1.0/. Using JavaScript and leaflet a map was created with colored markers to indicate earthquakes that have happened in the past week. The color of each of the markers corresponds with the depth of the earth quake and the size of the circle marker indicates the magnitude of the earth quake. A very simple web page was made to display the leaflet map. Role: Sole Author Tools: JavaScript, Leaflet, HTML5, API

The purpose of this project was to pull and clean data about food deserts to then use SQLAlchemy to transfer the data to a Postgres SQL Data base. We collected and cleaned 3 different data sets about areas that have low food access. The first data set was all the places with low food access, the second data set was heart disease rates in these areas, and the final data set was income levels of all these areas. We wanted the data to be eventual used to see how different factors effected food access. Role: Team Member Tools: Jupyter Note Book, Pandas, Python, SQLAlchemy