Overview
My concept this week is inspired by the past two semesters (and years) I have spent on graduate school applications. For my data, I sourced a list of self-reported applicant statistics for Computer Science programs. To preprocess the data, I used Python Pandas to drop unnecessary columns and rows that didn’t contain a GPA score. I also truncated the data to 10,000 rows, since I figured that 963,845 data points might be a bit unnecessary for my purposes.
df = (
pd
.read_csv('gradcafe.csv')
.drop(columns=['comment', 'gre_quant', 'gre_verbal', 'major', 'season', 'date_of_result', 'gre_awa', 'date_added', 'applicant_status'])
.dropna(subset=['undergrad_gpa'])
.query('decision == "Accepted" or decision == "Rejected"')
.query('undergrad_gpa <= 4.0')
.reset_index(drop=True)
)
df[:10000].to_csv('gradcafe_cleaned_10k.csv', index=False)
Discussion
The piece is centered around the at times reductionist nature of graduate school applications, and applications of other natures (e.g., visa applications). These applications often hinge major aspects of your life on how well a sheet of paper communicates your story to the fatigued person reading it on the other side, tasked with reviewing hundreds of applications in a single sitting. The stakes can feel prolific, where years of your life depend on these single decisions. Furthermore, applicants pour their life stories into these applications–providing another level of vulnerability.
I chose to use GPA as the reductionist metric in this case, which appears slowly as the person’s profile disappears. It emphasizes how these applicants, while not in all cases, often get reduced to numbers and metrics to quantitatively compare and evaluate. This process often does not acknowledge the nuance, story, and personhood of applicants.