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...

The National Survey of College Graduates is a repeated cross-sectional biennial survey that provides data on the nation's college graduates, with a focus on those in the science and engineering workforce. This survey is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

College Majors and their Graduates

Job Opportunities, Salaries and Gender Disparities

By FiveThirtyEight [source]

About this dataset

This repository contains a comprehensive selection of lavish data and processing scripts behind the articles, graphics, and interactive experiences generated by FiveThirtyEight. With this dataset, you'll have the power to explore college programs and their graduates like never before and create stories of your own! Whether you use it to check our work or craft your own powerful visuals, we would absolutely love to know if you found it useful. Under the Creative Commons Attribution 4.0 International License and MIT License respectively, our data is available for anyone who chooses to use it. Let us know how our resources turned out at

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Research Ideas

• Create an interactive comparison tool for researching college majors and their earning potential, so that prospective students can make informed decisions about what to study.
• Analyze the proportions of male and female graduates across different majors to uncover gender disparities in higher education.
• Explore the correlations between major categories, average salaries earned by graduates from specific major categories, unemployment rates for those with specific majors and more – to identify trends in job opportunities for certain specialties of study

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: majors-list.csv | Column name | Description | |:-------------------|:----------------------------------------------------| | FOD1P | First-level division of the field of study (String) | | Major | The specific major of the field of study (String) | | Major_Category | The broader category of the field of study (String) |

File: recent-grads.csv | Column name | Description | |:-------------------------|:-------------------------------------------------------------------------------| | Major | The specific major of the field of study (String) | | Rank | The rank of the major in terms of popularity (Integer) | | Major_code | The code associated with the major (Integer) | | Major_category | The category of the major (String) | | Total | The total number of students in the major (Integer) | | Sample_size | The sample size of the major (Integer) | | Men | The number of male students in the major (Integer) | | Women | The number of female students in the major (Integer) | | ShareWomen | The percentage of female students in the major (Float) | | Employed | The number of employed graduates from the major (Integer) | | Full_time | The number of full-time employed graduates from the major (Integer) | | Part_time | The number of part-time employed graduates from the major (Integer) | | Full_time_year_round | The number of full-time year-round employed graduates from the major (Integer) | | Unemployed | The number of unemployed graduates from the major (Integer) | | Unemployment_rate | The unemployment rate of graduates from the major (Float) | | Median | The median salary of graduates from the major (Integer) | | P25th | The 25th percentile salary of graduates from the major (Integer) | | P75th | The 75th percentile salary of graduates from the major (Integer) | | College_jobs | The number of college jobs held by graduates from the major...

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 dataset includes 1000 rows, with one row for each high school in the dataset. The graduation rates for each school were generated randomly, and are not based on any actual data.

Columns

ACT composite score

SAT total score

Parental level of education

Parental income

high school GPA

college gpa

years to graduate

This dataset could be useful for exploring trends in graduation rates over time, comparing graduation rates between different regions or states, or analyzing factors that may be associated with changes in graduation rates over time. However, it is important to keep in mind that the data is not based on actual data, and should be used for exploratory or educational purposes only.

source: http: https://roycekimmons.com/tools/generated_data/graduation_rate

cover image: https://pin.it/3y4a0ks

Statistics on postsecondary graduates, including the number of graduates, the percentage of female graduates and age at graduation, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

The Project Information Literacy (PIL) lifelong learning survey dataset was produced as part of a two-year federally funded study on relatively recent US college graduates and their information-seeking behavior for continued learning. The goal of the survey was to collect quantitative data about the information-seeking behavior of a sample of recent graduates—the strategies, techniques, information support systems, and best practices—used to support lifelong learning in post-college life. The dataset contains responses from 1,651 respondents to a 21-item questionnaire administered between October 9, 2014 and December 15, 2014. The voluntary sample of respondents consisted of relatively recent graduates, who had completed their degrees between 2007 and 2012, from one of 10 US colleges and universities in the institutional sample. Quantitative data are included in the dataset about the learning needs of relatively recent graduates as well as the information sources they used in three arenas of their post-college lives (i.e., personal life, workplace, and the communities in which they resided). Demographic information—including age, gender, major, GPA, employment status, graduate school attendance, and geographic proximity of current residence to their alma mater—is also included in the dataset for the respondents. "Staying Smart: How Today's Graduates Continue to Learn Once They Complete College," Alison J. Head, Project Information Literacy Research Report, Seattle: University of Washington Information School (January 5, 2016), 112 pages, 6.9 MB.

This dataset contains the total annual unduplicated headcount and share of enrollment for undergraduate and graduate students in credit-bearing courses at public community colleges and state universities in Massachusetts since 2014. Data are disaggregated by fiscal year, segment, institution, and enrollment level, reporting counts and percentages of total enrollment by gender, race/ethnicity, residency, and age.

This dataset is 1 of 2 datasets that is also published in the interactive Annual Enrollment dashboard on the Department of Higher Education Data Center:

1) Public Postsecondary Annual Enrollment: Detail 2) Public Postsecondary Annual Enrollment: Summary

Related datasets: 1) Public Postsecondary Fall Enrollment 2) Public Postsecondary Fall Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Annual enrollment refers to a 12 month enrollment period over one fiscal year (July 1 through June 30). - Figures published by DHE may differ slightly from figures published by other institutions and organizations due to differences in timing of publication, data definitions, and calculation logic. - Data for the University of Massachusetts are not included due to unique reporting requirements. See Fall Enrollment for HEIRS data on UMass enrollment. -The most common measure of enrollment is headcount of enrolled students. Annual headcount enrollment is unduplicated, meaning any individual student is only counted once per institution and fiscal year, even if they are enrolled in multiple terms. Enrollment can also be measured as full-time equivalent (FTE) students, a calculation based on the sum of credits carried by all enrolled students. In a fiscal year, 30 undergraduate credits = 1 undergraduate FTE, and 24 graduate credits = 1 graduate FTE at a state university.

In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

The number of postsecondary graduates, by institution type, International Standard Classification of Education (ISCED), Classification of Instructional Programs, Primary groupings (CIP_PG), status of student in Canada and gender.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

This dataset contains the total annual FTE and unduplicated headcount enrollment for undergraduate and graduate students in credit-bearing courses at public community colleges and state universities in Massachusetts since 2014. Data are disaggregated by fiscal year, segment, institution, and student attributes such as enrollment level, residency, age, race/ethnicity, and gender.

This dataset is 1 of 2 datasets that is also published in the interactive Annual Enrollment dashboard on the Department of Higher Education Data Center:

1) Public Postsecondary Annual Enrollment: Detail 2) Public Postsecondary Annual Enrollment: Summary

Related datasets: 1) Public Postsecondary Fall Enrollment 2) Public Postsecondary Fall Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Annual enrollment refers to a 12 month enrollment period over one fiscal year (July 1 through June 30). - Figures published by DHE may differ slightly from figures published by other institutions and organizations due to differences in timing of publication, data definitions, and calculation logic. - Data for the University of Massachusetts are not included due to unique reporting requirements. See Fall Enrollment for HEIRS data on UMass enrollment. -The most common measure of enrollment is headcount of enrolled students. Annual headcount enrollment is unduplicated, meaning any individual student is only counted once per institution and fiscal year, even if they are enrolled in multiple terms. - Enrollment can also be measured as full-time equivalent (FTE) students, a calculation based on the sum of credits carried by all enrolled students. In a fiscal year, 30 undergraduate credits = 1 undergraduate FTE, and 24 graduate credits = 1 graduate FTE at a state university. - For precise calculations and aggregations, use the FTE_RAW column. The FTE column is for display only.

This dataset provides information on the number of graduates from private colleges and universities in Qatar. The data is categorized by educational institution, nationality, gender, and year. It presents the number of graduates for each combination of these attributes, offering valuable insight into trends in graduation across different groups. The dataset can be used to analyze the graduation patterns of Qatari and non-Qatari students, as well as the gender distribution within different educational institutions.

College Scorecard

The College Scorecard dataset is provided by the U.S. Department of Education and contains information on nearly every college and university in the United States. The dataset includes data on student loan repayment rates, graduation rates, affordability, earnings after graduation, and more. The goal of this dataset is to help students make informed decisions about their college choice by providing them with clear and concise information about each school's performance

How to use the dataset

This dataset can help understand the cost of attending college in the United States, as well as the average debt load for students. It can also be used to compare different schools in terms of their graduation rates and repayment rates

Columns

• UNITID: Unit ID for institution
• INSTNM: Institution name
• CITY: City
• STABBR: State
• ZIP: Zip code
• OPEID: OPE ID for institution
• OPEID6: OPE ID for institution (6-digit)
• ACCREDAGENCY: Accrediting Agency
• INSTURL: Institution URL
• NPCURL: Net Price Calculator URL
• SCH_DEG: Highest degree awarded
• HCM2: Carnegie Classification 2010:** Basic
• MAIN: Carnegie Classification 2010:** Main
• NUMBRANCH: Number of branch campuses
• PREDDEG: Predominant degree awarded
• HIGHDEG: Highest degree awarded
• CONTROL: Control of institution
• ST_FIPS: State FIPS code
• REGION: Region
• LOCALE: Locale code
• LOCALE2: Locale code (multiple categories per state)
• CCBASIC: Carnegie Classification 2010:** Basic
• CCMAIN: Carnegie Classification 2010:** Main
• CCUGPROF: Carnegie Classification 2010:** Undergraduate Profile
• CCSIZSET: Carnegie Classification 2010:** Size and Setting
• HBCU: Historically Black College or University
• PBI: Predominantly Black Institution
• ANNHI: Tribal College or University
• TRIBAL: Tribal College or University (Public)
• AANAPII: Asian American and Native American Pacific Islander-Serving Institution
• HSIP: Hispanic-Serving Institution (HSI)
• NANTI: Native American-Serving Nontribal Institution
• MENONLY: Men only
• WOMENONLY: Women only
• RELAFFIL: Religious affiliation
• DISTANCEONLY: Distance-only
• CURROPER: Currently operating
• VETERAN: Veteran-supportive
• LIMDEP: Limited-degree-granting
• HIGHDEG_GRANTED: Highest degree granted
• PS: Predominantly two-year public
• UGRD_ENRL_TOTAL: Undergraduate total enrollment
• GRAD_ENRL_TOTAL: Graduate total enrollment
• UGRD_ENRL_ORIG_YR2_RT: Undergraduate, first-time, first-year retention rate (%)

Acknowledgements

This data was originally collected by the US Department of Education and made available on their website. Thank you to the US Department of Education for making this data available!

This dataset provides information about Massachusetts public high school graduates enrolling into institutions of higher education by student group since 2004. It includes the count and percentage of students enrolled in any college or university, as well as a breakdown of enrollment in private vs. public, two-year vs. four-year, and Massachusetts vs. out-of-state institutions. It also includes the percentage of students enrolled in a Massachusetts community college, a Massachusetts state university, or the University of Massachusetts system.

The data provided in the report are based on point-in-time matching of graduates with higher education enrollment data from the National Student Clearinghouse (NSC). For more information about working with NSC data, including coverage and FERPA implications, please visit their Working with Our Data page.

Results are not reported for higher education enrollments of fewer than 15. Prior to the 2015 high school graduating class, the data refers to students attending college within 16 months of graduating high school. From 2015 on, the data is also provided by high school graduates attending college by the March following their high school graduation year. The percentages in the report are available by college attendee or high school graduate.

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset contains the same data that is also published on our DESE Profiles site: Graduates Attending Higher Ed

The Baccalaureate and Beyond Longitudinal Study 1993, Base Year (B&B:93) is part of the Baccalaureate and Beyond Longitudinal Study (B&B) program. B&B:93 (https://nces.ed.gov/surveys/b&b/) is a base year of a longitudinal study that followed a cohort of graduating college seniors who participated in the 1993 National Postsecondary Student Aid Study (NPSAS:93). The 1993 National Postsecondary Student Aid Study (NPSAS:93) data provided the base-year sample for B&B:93. NPSAS:93 data are representative of all undergraduate and graduate students enrolled in postsecondary institutions in the 50 United States, the District of Columbia, and Puerto Rico that were eligible to participate in the federal financial aid programs in Title IV of the Higher Education Act, and the B&B cohorts is a representative sample of graduating seniors in all majors. Key statistics produced from B&B:93 are information on bachelor's degree recipients' undergraduate experience, demographic backgrounds, expectations regarding graduate study and work, and participation in community service.

The number of postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED), age group and gender.

This dataset offers a set of statistics on the number of students enrolled from 2006-07 to 2022-23 per public institution under the supervision of the French Ministry of Higher Education: universities, Technology Universities, Large Institutions, COMUE, Normal Graduate Schools, Central Schools, INSA, Other Engineering Schools... Unless otherwise noted, the indicators proposed in this dataset do not take into account double CPGE registrations The number of students enrolled in parallel in IFSI (Institutes for Nursing Training) is not taken into account in the number of institutions. **** The data are taken from the Student Monitoring Information System (SISE). Registrations are observed on January 15, except for the University of New Caledonia, which has additional time to take into account the Southern calendar. Each line of this dataset provides an institution’s statistics for one academic year. This game unitely declines a set of variables on the student (sex, baccalaureate, age at the baccalaureate, national attractiveness, international attractiveness) and the training he mainly follows (cursus LMD, type of diploma, diploma, major discipline, discipline and disciplinary sector). The geographical data provided in this game relate to the seat of the institution and not the actual location of the training followed by the student. Cross-sectional and more detailed data are available in the dataset “Staff of students enrolled in public institutions under the supervision of the Ministry of Higher Education](https://data.enseignementsup-recherche.gouv.fr/explore/dataset/fr-esr-sise-effectifs-d-etudiants-inscrits-esr-public/)”. National Framework of Training and Conventions EPSCP-CPGE: impacts on measured workforce changes Two regulatory provisions impact developments from 2018-19 onwards and create statistical breaks: - The new National Training Framework (CNF), put in place for Bachelor’s degrees. The CNF significantly reduces the number of diploma titles. Some of these titles have become more precise, leading to an easier ranking by discipline: this is the case for science licences, less frequently classified in “Plurisciences”, but more in “fundamental sciences and applications” or “sciences of nature and life”. On the other hand, other titles are more general, particularly in literary disciplines (e.g. license mention Humanities) and are more frequently classified as “plurilettres, languages, humanities”. - The progressive implementation of agreements between high schools with preparatory classes for the Grandes écoles (CPGE) and the public institutions of a scientific, cultural and professional nature (EPSCP), of which universities belong, significantly increases the number of LMD license registrations from this year onwards, even if double enrolments were already possible and effective before. University enrolments include these double registrations. These two developments mainly impact the workforce detailed by discipline in L1, which hosts the vast majority of new entrants. The impact on total staff is more marginal. Developments taking into account double listings are at constant regulatory scope. — In 2015-2016 the 2014-15 data for these institutions were renewed: University of New Caledonia, ENS Cachan, ENS Rennes. For more information on this dataset, see dataset documentation.

This dataset provides the number of graduates from public colleges and universities in Qatar, broken down by academic degree, academic program, educational institution, nationality, gender, and year. It offers valuable insights into the trends in public higher education, including the distribution of graduates across different academic programs and institutions, and the demographic breakdown by nationality and gender.

This dataset presents the number of students enrolled annually in each school at Doha Institute for Graduate Studies, categorized by intake year.