During my time as an undergraduate student at Butler University, I worked for two summers on computational astronomy projects. The first summer I spent adding features to a simulation of the growth of a supermassive black hole written in FORTRAN and learning how to present data using Supermongo, a plotting package often used by astronomers.

The second summer, I was an REU (Research Experiences for Undergraduates) student with the Southeastern Association for Research in Astronomy (SARA). For this project, I wrote a program in C++ to calculate the x-ray output of star, shell scripts to run this program with many different parameters and to analyze the data, and Supermongo code to visualize the data. I then wrote an article describing my findings and presented a poster at the winter conference for the American Astronomical Society in 2011.

While in graduate school at Ohio State, I spent four years working both individually and as part of a group to collect data on point defects and conductivity in strontium titanate. I would then use Origin and Excel to analyze and visualize the collected data and present them to my advisor and the rest of the research group.

Shortly after finishing my master’s in physics, I was looking for resources to improve my coding skills and found one titled “Python for Data Science.” This term intrigued me and as I researched it, I found that it is the combination of using programming for working with data and doing science which is exactly what I was looking for. My degrees in physics have prepared me well for being a problem solver in a scientific setting; however, I did not gain the requisite knowledge of many of the tools used in professional analytics. In pursuit of my personal passion and professional goals, I began taking online courses covering many topics, including Excel, Tableau, R, Python, SAS, and SQL. I have attended hackathons in my free time, as well as Meetups geared towards problem solving and knowledge sharing in Python.