College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

Institute Graduation Rate Prediction Dataset is prepared from IPEDS[1] dataset by following proposed framework[2] by** Ms. Mala H. Mehta, Dr. N.C.Chauhan and Dr.Anu Gokhle** (Research Paper presented in ET2ECN-2021 International Conference). The paper will soon be published in Springer-Scopus Indexed publication.

The dataset consists of total 143 features and 11319 records of 8 student batches (from 2004 to 2011). How many students have successfully graduated within stipulated time period? Can we do the prediction of that? If low graduation rates are known in advance, institute can take prior steps to avoid low graduation rates.

Cite this dataset as - Ms. Mala Mehta Bhatt, Dr. N.C.Chauhan, & Dr. Anu Gokhale. (2021). Institute Graduation Rate Prediction Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/2914166

1 Objective 1.1 Context Education data mining (EDM) is a field related to generate useful,novel and actionable knowledge by applying miniing/ML algorithms on academic data. Knowledge generated could give unexpected benefit to education domain stakeholders.

EDM also known sometimes as Learning Analytics has various branches to work. Two Major branches are: 1. Student Performance related study 2. Institute Performance related study. Much research is done on the first aspect, however, the second aspect is not touched much.

This dataset is designed with aim of effectively predicting Institute Graduation Rates for Higher education institutions.

2 IPEDS [1] Dataset The National Centre for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations.

The Integrated Postsecondary Education Data System (IPEDS) surveys approximately 7,500 postsecondary institutions, including universities and colleges, as well as institutions offering technical and vocational education beyond the high school level. IPEDS, which began in 1986, replaced the Higher Education General Information Survey (HEGIS).

IPEDS consists of nine integrated components that obtain information on who provides postsecondary education (institutions), who participates in it and completes it (students), what programs are offered and what programs are completed, and both the human and financial resources involved in the provision of institutionally-based postsecondary education.

3 Approach 3.1 Feature Selection IPEDS dataset is a big dataset consisting of many tables and many years' databases. A framework[2] was designed to extract IGR related features and data. By following this framework, final file was created. 143 Features were selected out of which one is response variable. 3.1.1 Response Variable GBA4RTT - Graduation rate - bachelor's degree within 4 years 3.1.2 Predictor Variables 142 Predictor/Independent features are identified. (meta data is uploaded.)

3.2 Handling Missing Values Missing values are handled by applying statistical measure mean on each feature and the replacing missing values by them. 3.3 Splitting into Train-Validation-Test sets Data is split into training and testing set with 80-20% ratio. 3.4 Modeling AS Response variable considered in the study is a continuous variable. Regression Models are used to find the minimum error in prediction. 4 models are considered: Multiple linear regression, Support vector regression, Decision tree regression, XGBoost regression 4 Execution Execution process consists of below mentioned step by step procedure: 1. Preprocessing of data, 2. Splitting the data in training and testing sets, 3. Applying the models, 4. Measuring MSE,RMSE,R2, Adjusted R2 and program's running time. 5 Conclusion Mean Squared Error measured is considered here for comparison among 4 models. Minimum MSE is received in XGBoost regression algorithm followed by support vector regression, decision tree regression and multiple linear regression algorithms. Future Work Researchers could use the dataset for further analysis with different models, different dimensionality reduction techniques and education domain analysis. References [1] NCES, “National Center for Education Statistics”, Available at: https://nces.ed.gov/ipeds/use-the-data, Accessed at 2021. [2] "A Dataset preparation framework for education data mining" presented in 4th international conference on Emerging technology trends in electronics, communication and networking (ET2ECN-2021), SVNIT, Surat. Acknowledgements Thanks to NCES [1], for providing such huge open repository related to education available freely. I acknowledge all efforts put by Dr. N.C.Chauhan and Dr. Anu Gokhale in this work. Special Thanks to Vinay Bhatt, who found IPEDS repository for me, because of that only I was able to prepare this dataset.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical _location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2018-2019 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from colleges, universities, and technical and vocational institutions that participate in federal student financial aid programs under the Higher Education Act of 1965 (as amended). The NCES EDGE program uses address information reported in the annually updated IPEDS directory file to develop point locations for all institutions reported in IPEDS. The point locations in this data layer were developed from the 2022-2023 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

I downloaded this data from the ElSi (Elementary/Secondary Information System) tableGenerator hosted by the Institute of Educational Sciences' National Center for Education Statistics. ELSI tableGenerator

The cleaned, analysis-ready files are "finances_2001_2017.csv" and "pupils_fte_teachers_2001_2019.csv".

I am going to add graduation rate data. This is for an undergrad project on marijuana legalization and high school graduation rates.

Variable Definitions: "Total Expenditures (TE11+E4D+E7A1) per Pupil (MEMBR) [State Finance] This is the Total Expenditures (Digest) divided by the fall membership as reported in the state finance file. The Total Expenditures (Digest) is the subtotal of Direct State Support Expenditures for Private Schools (e4d), Debt Services Expenditures - Interest (e7a1) and Total Expenditures for Education (te11). These data are from the CCD National Public Education Financial Survey."

"Total revenues per student are the total revenues from all sources (tr) divided by the fall membership as reported in the state finance file. These data are from the CCD National Public Education Financial Survey."

"Full-Time Equivalent (FTE) Teachers [State] This is the total number of full-time equivalent teachers in a state as defined by the CCD State Nonfiscal Survey."

"Grades 9-12 Students [State] This is the number of students in a state who are enrolled in ninth grade through twelfth grade. These data are taken from the CCD State Nonfiscal survey."

#### Wallpaper by NeONBRAND on Unsplash

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects graduation rate data which provides information on institutional productivity.

Over the past five years, for-profit universities have faced mounting headwinds amid regulatory tightening, inflation and negative public perception. While data from the National Center for Education Statistics (NCES) reports that overall postsecondary enrollment grew by just 0.5% from 2020 to 2025, enrollment at for-profit institutions shrank by 4.1%. Ballooning student debt and rising tuition, made worse by inflation in 2022 and 2023, have driven many recent graduates and adult learners to second-guess the value of higher education, especially degrees from for-profit schools with poor graduate earnings. Government regulations added further strain as the Biden administration's 2024 reinstatement of gainful employment rules once again linked access to federal funding to graduate debt-to-income ratios. At the same time, for-profit schools battled declining revenue as affordable nonprofit and vocational programs drew away budget-conscious students. Industry revenue has dropped at a CAGR of 0.5% to an estimated $13.6 billion over the five years through 2025. A faltering reputation has played a major role in the industry's decline. According to Federal Student Aid data, for-profit universities are repeatedly criticized for low graduation rates, weak graduate earnings and high student loan default rates—the highest across any demographic. Allegations of predatory practices remain in the headlines, exemplified by Walden University's $28.5 million lawsuit settlement in 2024. Although these institutions offer flexible scheduling and lower tuition rates that appeal to low-income and nontraditional students, the public remains wary. Studies indicate that most programs with no positive return on investment are at for-profit colleges. Meanwhile, stricter government scrutiny and the widespread availability of earnings and debt data have made poor outcomes highly visible, solidifying the negative perception. Many for-profit universities have shuttered, though some have managed to retain profit by closing physical locations. For-profit universities will continue facing a decline over the next five years. IBISWorld expects for-profit university enrollment to drop at an annualized 1.1% through 2030, outpaced by modest growth at nonprofit and vocational schools, where graduates see better employment outcomes. Uncertainty in regulations, including the possible repeal of the 90/10 rule, adds more volatility, while the lack of broad student loan forgiveness will likely suppress affordability and demand. As students and job seekers prioritize educational outcomes and cost, one in seven for-profit universities is expected to close by 2030. For-profit universities' revenue is set to sink at a CAGR of 0.3% to an estimated $13.4 billion through the next five years.

Graduation rate data, 150% of normal time to complete - cohort year 2011 (4-year) and cohort year 2014 (2-year) institutions (revised October 2019)

Obtained from: https://nces.ed.gov/ipeds/datacenter/DataFiles.aspx

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). The NCES EDGE program uses address information reported in the annually updated IPEDS directory file and collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for all institutions reported in IPEDS. The point locations in this data layer were developed from the 2016-2017 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical _location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2017-2018 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Context

College tuition data is somewhat difficult to find - with many sites limiting it to online tools.

Content

The data this week comes from many different sources but originally came from the US Department of Education. The most comprehensive and easily accessible data cames from TuitionTracker.org who allows for a .csv download! Unfortunately it's in a very wide format that is not ready for analysis, but tidyr can make quick work of that with pivot_longer(). It has a massive amount of data, I have filtered it down to a few tables as seen in the attached .csv files. Tuition and diversity data can be quickly joined by dplyr::left_join(tuition_cost, diversity_school, by = c("name", "state")). Some of the other tables can also be joined but there may be some fuzzy matching needed.

Historical averages from the National Center for Education Statistics (NCES) - spanning the years 1985 - 2016.

• Tuition and fees by college/university for 2018-2019, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education.
• Diversity by college/university for 2014, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education.
• Example diversity graphics from Priceonomics.
• Average net cost by income bracket from TuitionTracker.org.
• Example price trend and graduation rates from TuitionTracker.org
• Salary potential data comes from payscale.com.

Acknowledgements

The data was downloaded and cleaned by Thomas Mock for #TidyTuesday during the week of March 10th, 2020. You can see the code used to clean the data in the TidyTuesday GitHub repository.

Inspiration

Use this dataset to explore the costs of college tuition in the US on their own, by geographic area, degree type, and/or salary. Whatever you choose to explore, consider sharing your notebook on Twitter using the #TidyTuesday hashtag!

License

The data provided in the TidyTuesday repository is licensed under the MIT License.

Business problem

Most American institutions (that are not necessarily the top 10-20) that provide undergraduate education face a challenge i.e. what kind of students they will make an offer for admission so that they can maintain a good performance in terms of number of students completing their courses in time. They also need to predict how the current batch of intake will perform. The universities cannot relax the entry criteria too much as that way the quality of education that they provide gets diluted. At the other hand, they have to make offers to the candidates who are not only having appropriate profile but are also most likely to accept the offers.
On the other side, the students have a challenge deciding which colleges they should apply i.e. the colleges that provide best performance at a minimal cost given their own profiles. The student profile is determined by not only the performance in examination such as SAT and ACT, but also other data points such as their ethnicity, immigration status, gender etc.
We will be interested in answering three main questions for an institute i.e.

(a) What will be a likely enrollment rate?
(b) What will be a likely graduation rate? (c) Which are the most lucrative colleges for students in terms of pass rate and cost?

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Both (a) and (b) are regression problems as we are trying to predict values of continuous variables.

The dataset

Use the dataset provided at https://public.tableau.com/en-us/s/resources It is called “American University Data”. This is a smaller version of dataset available at https://nces.ed.gov/ipeds/Home/UseTheData. If you need explanation about the dataset, refer to this parent site.

The Schools and Staffing Survey, 2007-08 (SASS 07-08), is a study that is part of the Schools and Staffing Survey (SASS) program. SASS 07-08 (https://nces.ed.gov/surveys/sass/) is a cross-sectional survey that collects data on public, private, and Bureau of Indian Education (BIE) elementary and secondary schools across the nation. The survey was primarily conducted through the use of mailed paper questionnaires. Nonresponse follow-up interviews were conducted using computer-assisted telephone interviews and face-to-face paper interviews. Teachers, librarians, principals, and districts were sampled. Key statistics produced from SASS 07-08 included how many teachers remained at the same school, moved to another school, or left the profession in the year following the SASS administration.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2019-2020 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

About

This dataset provides a comprehensive look at U.S. universities in 2023 by merging data from two authoritative sources:

• IPEDS (Integrated Postsecondary Education Data System): A dataset from the U.S. Department of Education that includes extensive institutional data on enrollment, tuition, graduation rates, and more.
• ARWU (Academic Ranking of World Universities) 2023: known as a Shanghai Rankings, which ranks universities based on their academic performance, research, etc. The university names have been modified to match IPEDS data, allowing for seamless merging and analysis.

What's included

• University names, locations
• Classifications (public/private, size category, etc.)
• Applications, Admissions, Enrollment statistics
• Tuition, fees, costs of attending
• Degrees awarded (number of bachelor’s, associate, etc.)
• SAT and ACT test scores
• 2023 ARWU Ranking

Note: I tried to choose (in my opinion) the most important features from the IPEDS dataset. If you feel like there's something missing you can:

A) add suggestions for features I should include (see variables available at: https://nces.ed.gov/ipeds/use-the-data)

B) use this link (https://nces.ed.gov/ipeds/use-the-data) to get your own customized version of the dataset, which you can then merge with my shanghai_ranking.csv

Data Sources

• IPEDS 2023: Official data retrieved from the National Center for Education Statistics (NCES).
• ARWU 2023: Data modified to ensure university names match IPEDS records for accurate merging.

I'd appreciate an upvote if you found the dataset useful/helpful/interesting and any suggestions for improvements are welcome.

The Fast Response Survey System (FRSS) was established in 1975 by the National Center for Education Statistics (NCES), United States Department of Education. FRSS is designed to collect issue-oriented data within a relatively short time frame. FRSS collects data from state education agencies, local education agencies, public and private elementary and secondary schools, public school teachers, and public libraries. To ensure minimal burden on respondents, the surveys are generally limited to three pages of questions, with a response burden of about 30 minutes per respondent. Sample sizes are relatively small (usually about 1,000 to 1,500 respondents per survey) so that data collection can be completed quickly. Data are weighted to produce national estimates of the sampled education sector. The sample size is large enough to permit limited breakouts by classification variables. However, as the number of categories within the classification variables increases, the sample size within categories decreases, which results in larger sampling errors for the breakouts by classification variables. The Secondary School Arts Education Survey, Fall 2009 data provide national estimates on student access to arts education and the resources available for such instruction in public secondary schools during fall 2009. This is one of a set of seven surveys that collected data on arts education during the 2009-10 school year. In addition to this survey, the set includes a survey of elementary school principals, three elementary teacher-level surveys, and two secondary teacher-level surveys. A stratified sample design was used to select principals for this survey. Data collection was conducted September 2009 through June 2010, and 1,014 eligible principals completed the survey by web, mail, fax, or telephone. The secondary school survey collected data on the availability of music, visual arts, dance, and drama/theatre instruction; enrollment in these courses, the type of space used for arts instruction, the availability of curriculum guides for arts teachers to follow, and the number of arts teachers who are specialists in the subject. Principals reported on graduation requirements for coursework in the arts; school or district provision of teacher professional development in the arts; and arts education programs, activities, and events. Principals also reported on community partnerships and support from outside sources for arts education. Furthermore, principals were also asked to provide administrative information such as school instructional level, school enrollment size, community type, and percent of students eligible for free or reduced-price lunch.

Data from https://github.com/rfordatascience/tidytuesday/edit/master/data/2021/ released under an open license: https://github.com/rfordatascience/tidytuesday/blob/master/LICENSE

College Enrollment

The data this week comes from Data.World and Data.World and was originally from the NCES.

High school completion and bachelor's degree attainment among persons age 25 and over by race/ethnicity & sex 1910-2016

Fall enrollment in degree-granting historically Black colleges and universities (HBCU)

Consider donating to HBCUs, to help fund student's financial assistance programs.

Donation link: https://thehbcufoundation.org/donate/

There's other additional HBCU datasets at Data.World as well.

HBCU Donations Article

... Donation will be placed in an endowment for students to fund need-based scholarships. President Reynold Verret believes the donation will provide an opportunity for students who don’t have the same financial support as others.

“Xavier has roughly more than half of our students who are Pell-eligible. Which means they are in the lowest fifth of the socioeconomic ladder in the country. The lowest quintile. So these students really have significant family needs,” said Verret. “They’re often the first generation in their families to attend college, and meeting the gap between what Pell and the small loans provide and making it affordable is where that need-based is, which is not just based on merit, on your highest ACT or GPA, but basically to qualify students who are able who have the talent and the ability to succeed at Xavier.”

I've left the datasets relatively "untidy" this week so you can practice some of the pivot_longer() functions from tidyr. Note that all of the individual CSVs that are duplicates of the raw Excel files.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-02-02')
tuesdata <- tidytuesdayR::tt_load(2021, week = 6)

hbcu_all <- tuesdata$hbcu_all

# Or read in the data manually

hbcu_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-02/hbcu_all.csv')

Data Dictionary

hbcu.csv

hs_students.csv

• The percentage of students broken down by race/ethnicity, aged 25 and over who have graduated HS.

bach_students, female_bach_students, female_hs_students, male_bach_students, male_hs_students:

• Same as above, but for specific gender and education combination.