"I worked on multiple data-related projects during my internship at Seton Education Partners. Using Python’s Pandas and Matplotlib packages, I analyzed data from parent surveys that evaluated students’ growth in various virtues. This involved retrieving data from Google Drive; cleaning the data; creating pivot tables that summarized results for various groups; performing regression analyses; and representing the data in line, bar, and pie graph form. In addition, as part of a data warehouse setup process, I used SQL to compare data from two different databases. I also made edits to a Python program used to update staff and student Google accounts so that it would better match the organization’s preferences. Other shorter projects included creating a presentation on data security for staff members; analyzing census data for two zip codes, the latter of which involved accessing US census data via Python; designing a Python script that generated a comprehensive list of files and folder paths to assist with a file transfer process; and writing another Python script to generate lists of students for enrollment purposes.

Using Python greatly helped me speed up my workflow once I had the initial code in place. For instance, certain projects required me to create over a dozen charts. With Python, once I had a code block for creating one such chart, I could then feed data into a ‘for’ loop to create all the other charts. This also meant that changing 19 charts was often just as easy as changing one chart. Writing Python code to complete these projects should make it easier to perform similar tasks in the future. For instance, rather than recreating a series of pivot tables in Excel each year to analyze student character data, users could instead run the set of scripts that I created. While some tweaks to the scripts will likely be necessary, Python should help speed up these projects. Data is an important element of Seton’s mission, so these projects corresponded well with the organization’s goals and needs. For example, once the data warehouse project is complete, it will enable school and network staff to easily access relevant metrics in a centralized location.

During my internship, I made extensive use of the Python and SQL skills I built up through Professor Mattan Griffel’s Intro to Python and Intro to Databases courses. Before 2021, I didn’t have any Python or SQL programming experience, although I had studied C++ on my own. Thanks to Professor Griffel’s instruction, I was ready to apply both languages in a professional context by the time my internship began. The Statsmodels, Pandas, and Matplotlib Python libraries that we learned in class proved highly useful for my data analysis projects. In addition, our practice with APIs contributed to my use of the Google Sheets, Google Slides, and Census APIs, all of which made my work easier.

In order to perform linear and logistic regressions of student data, I applied the knowledge gained through my Intro to Statistics, Business Analytics, and Applied Regression Analysis classes. The insights that I gained through Business Analytics II and the Analytics Advantage also made me more confident in my ability to analyze information via Python.

Using Python in place of Excel proved to be a challenge at times, simply because I am still relatively new to the language. Fortunately, because Python is so popular, I found plenty of online resources and Python libraries to help me complete tasks more efficiently and decode error messages. Similarly, I needed to learn how to use the Microsoft and Oracle SQL implementations, which differed somewhat from the SQLite and PostgreSQL that we covered in Professor Griffel’s class. Keeping data secure was also a challenge. Since much of the data I was analyzing was confidential, I needed to make sure that others could not access this data. To respond to this challenge, I applied a number of tools to make my data analysis operations more secure. First, I encrypted my hard drive using VeraCrypt. Second, I installed the Eraser tool in order to securely wipe records from my hard drive. Perhaps most importantly, I learned how to use the Google Sheets API to download and upload data as needed. That way, I wouldn’t need to keep personally identifying information on my hard drive. David Morales, the senior director of data management and analytics, provided excellent supervision and guidance throughout my internship. I particularly appreciated his openness to the use of Python to perform tasks that might otherwise be accomplished using Excel or Google Sheets

One insight I gained even before the internship began was the power of networking. Earlier this year, I scheduled a coffee chat with David to learn more about his work. By the end of that conversation, we realized that Seton could be a great internship location for me. Although Seton did not have a formal internship program at that time, I was able to come on board through that initial networking contact. Another takeaway is that I greatly enjoy working with Python to solve data analytics challenges. It’s very satisfying to develop a Python script that will automatically complete tasks that would take a long amount of time in Excel, and I’ve enjoyed building up my programming skills throughout my internship. Furthermore, I’ve realized that the data analytics field greatly appeals to me. I came to CBS knowing that I wanted to pivot to a leadership-related role in the nonprofit sector, but I wasn’t sure which functional area (finance, development, marketing, HR, etc.) would most appeal to me. I now realize that working with data is not only an essential skill for organizations, but a challenge that I enjoy, especially when I’m able to use Python to complete analytics tasks. My hope is to continue working with data after I graduate, and I’m sure that this internship will help me accomplish that goal."