GradCafe is a platform where grad school applicants share admission status and communicate updates. One thing all applicants need is information - when to expect an interview, whether being put in a waiting list or receiving an email starting with Congratulations. The uncertainty that comes with these questions can be relieved to certain degree with information from other applicants.
As freshly admitted grad students, we are curious about what we can know about application in Statistics over the last decade from GradCafe, where we have access to 10,000 application results from 2010 to 2020 with information on the program, year, applicant undergrad GPA, GRE result, admission status, etc.
We also incorporate two major university rankings in Statistics: U.S. News and QS World University Rankings as these rankings are reliable indicators of academic reputation of a particular university and program, which can affect decisions of both applicants and programs significantly. Besides, these two rankings are selected to represent the major domestic and international evaluation of universities.
With three datasets on admission result and ranking scraped, we are able to answer two major questions below.
- What kind of programs are popular among the applicants in Statistics?
- What kind of applicants are usually preferred by Statistics graduate programs?
| Institution | Program | Degree | Season | Admission Status | Admission Via | Status | Date Added | Notes |
|---|
Institution: name of the Institution
Program: name of the program
Degree: Master's, doctoral, or others
Season: fall or spring in which year
Admission Status: accepted, rejected, waitlisted, or others
Admission Via: email, website, or others
Status: A, R, W, O (abbr. of admission status)
Date Added: the date to report this admission result
Notes: a note left by the poster
| Rank | Name | Score | Country | District |
|---|
Rank: the rank of the university
Name: name of the university
Score: score of the university
Country: country of the university
District: city and state where the university is located in
| Rank | Name | Score | District |
|---|
Rank: the rank of the university
Name: name of the university
Score: score of the university
District: city and state where the university is located in
| Institution | Location | Year | Rank | Overall Score | Academic Reputation | Employer Reputation | Citation per Paper | H-index citation |
|---|
Institution: name of the Institution
Location: country of the university
Year: year of ranking
Rank: the rank of the university
Overall Score: the final score that decides the ranking
Academic Reputation: score in academic reputation
Employer Reputation: score in employer reputation
Citation per Paper: score in citation per paper
H-index citation: score in h-index citations
clean.py: data cleaning of Gradcafe dataset
crawler.py: scraper of Gradcafe
crawler_usnews.py: scraper of US News overall ranking
crawler_usnews2.py: scraper of US News ranking in Statistics
plot_functions.py: contain functions for generating plots in jupyter notebooks
plot_geopandas.py: functions to generate national maps
qs_ranking_scraper.py: scraper of QS ranking in Statistics
states_province: data on states to generate national maps
USnew_overall_rank.csv: US News overall ranking
USnew_stat_rank.csv: US News ranking in Statistics
gradcafe.csv: Gradcafe dataset
qs_rank.csv: QS ranking in Statistics
analysis.ipynb: analysis and report