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.

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system. Provides aggregated key enrolment data for college students, such as: * Fall term headcount enrolment by campus, credential pursued and level of study * Fall term headcount enrolment by program and Classification of Instructional Program * Fall term headcount enrolment by student status in Canada and country of citizenship by institution * Fall term headcount enrolment by student demographics (e.g., gender, age, first language) To protect privacy, numbers are suppressed in categories with less than 10 students. ## Related * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * School enrolment by gender * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

Dataset Description

This dataset was created by collecting publicly available university and college information from the U.S. Department of Education’s College Scorecard. It focuses on higher education institutions across the United States as of 2025.

The dataset provides a comprehensive view of universities and colleges, including details on cost, graduation rates, and post-graduation earnings — helping students, researchers, and policymakers analyze educational outcomes and institutional performance nationwide.

The dataset contains 5546 records and includes the following key features:

University_Name: The official name of the college or university.

Location: The city and state where the institution is located.

Type: Indicates the nature of the institution — Public, Private Nonprofit, or For-Profit.

Place: Describes the campus setting, such as City, Suburban, or Rural.

Group: Represents the size or category of the institution (e.g., Small, Medium, Large).

Graduate_Rate: The percentage of students who successfully complete their degree programs.

Annual_Cost: The average yearly cost of attendance, including tuition and fees.

Earnings: The median annual earnings of graduates after completing their degree programs.

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.

This dataset provides the number of students enrolled in private colleges and universities in Qatar, categorized by educational institution, nationality, and gender. The data includes institutions such as Education City Universities, Hamad Bin Khalifa University, and Lusail University. It allows for the analysis of student enrollment trends across different institutions, nationalities (Qatari and Non-Qatari), and genders. This dataset is useful for understanding the distribution of students in Qatar's higher education institutions, as well as the participation of male and female students within these institutions.

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.

University and college enrollment measures the total fall enrollment at the University of Illinois and Parkland College. Spring enrollment figures are not included in this indicator.

Enrollment at the University of Illinois has seen a steadily increasing trend since 2000, with a slight drop between 2006 and 2007, and again between 2022 and 2023. Enrollment at Parkland College has had less of a consistent trend, fluctuating between 2000 and 2011. Parkland College enrollment saw a sharp increase between 2011 and 2012, and decreased between 2012 and 2022. Parkland College enrollment increased in 2023 and 2024.

The University of Illinois and Parkland College are both important parts of the Champaign-Urbana community. Enrollment is an intuitive measure of an institution’s growth. Enrollment cannot paint a complete picture of any institution, but it is a solid starting point.

This data came from enrollment reports posted on the University of Illinois and Parkland College websites.

Sources: Parkland College. Website. Table A1: Annual Enrollment Data. FY 00 - FY 24. (Retrieved 5 December 2024).; Division of Management Information. University of Illinois at Urbana-Champaign. Website. UIUC Student Enrollment. Years 2000-2024. (Retrieved 5 December 2024).

About this dataset

Context

The dataset tabulates the State College population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of State College across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of State College was 40,687, a 0.77% increase year-by-year from 2022. Previously, in 2022, State College population was 40,378, a decline of 1.54% compared to a population of 41,011 in 2021. Over the last 20 plus years, between 2000 and 2023, population of State College increased by 1,405. In this period, the peak population was 42,559 in the year 2016. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the State College is shown in this column.
• Year on Year Change: This column displays the change in State College population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State College Population by Year. You can refer the same here

This dataset contains the total Fall enrollment headcount and FTE for undergraduate and graduate students at all public Massachusetts institutions of higher education since 2014.

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

Public Postsecondary Fall Enrollment Public Postsecondary Fall Enrollment by Race and Gender

Related datasets: Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Figures published here may vary from those of individual institutions due to differences in calculation methodologies.

This dataset contains data on the number of students in schools and universities categorized by level of education (Pre-primary, Primary, etc.), type of education (Government, Private), and gender (Male, Female). The data provides insight into the enrollment trends across different education levels and types of schools in the region. This dataset is essential for analyzing gender and educational distribution within both government and private institutions.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_f5db67df93abf6d78346c5ef83a4ca25/view

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

This dataset provides information for Academic Years 2017-2021 which included: By College and VCCS System:

1) Annual Headcount and FTEs 2) Gender (categories are: Female & Male; Unknown may be inferred) 3) Ethnicity (categories are: American Indian & Alaskan Native, Asian, Black & African-American, Native Hawaiian & Pacific Islander, Hispanic, Two or More Races, Unknown/Not Specified, and White) 4) Age (categories are: 17 and Under, 18-19, 20-21, 22-24, 25-29, 30-34, 35-39, 40-49, 50-64, & 65 and Over) 5) 18-Month Outcomes for Dual-Enrolled High School Grads by Year (categories are: Total Grads, Continued in any Higher Ed program, Employed with no Higher Ed, and Unknown) 6) 18-Month Outcomes for VCCS Graduates by Year (categories are: Total Grads, Continued at VCCS, Transferred to a 4yr college, Employed with no Higher Ed, and Unknown)

For Fiscal Years 2018-2021, by Service Area and VCCS System:

1) Fast Forward Credentialers Employed by Fiscal Year (categories are: Total Distinct Students, Employed within 6 Months, Employed within 12 Months, and Employed within 18 Months)

Notes:

1) Headcounts are Unduplicated student counts. 2) One FTE represents 30 credit hours of classes taken by a student over an academic year and is calculated on an annual basis by taking the total credit hours taught divided by 30. 3) 2017 Fiscal Year Fast Forward data was not included as it was considered incomplete- the Fast Forward program began in 2017 and did not encompass all areas for the entire year.
4) In Workforce (Fast Forward data) the service region for the Richmond Metro Area is called CCWA (Community College Workforce Alliance) and combines data for Brightpoint and J Sargeant Reynolds. 4a) Therefore, there are no Reynolds data entries for Fast Forward variables. All CCWA data is listed under Brightpoint for this portion of the data set.
5) 18-Month Outcomes for Fast Forward Credentialers are cumulative (6 months to 12 months to 18 months)

Postsecondary enrolments, by detailed field of study, institution, institution type, registration status, program type, credential type, status of student in Canada and gender.

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300,000+ graduates from higher education institutions every year. Higher education opportunities range from two year, public and private institutions, four year, public and private institutions and regional education centers. Collectively, Maryland's higher education facilities offer every kind of educational experience, whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland is proud that nearly one-third of its residents 25 and older have a bachelor's degree or higher, ranking in the top 5 amongst all states. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live, learn, work and raise a family.

Number of Students: West Bengal: Colleges data was reported at 1,795,258.000 Person in 2021. This records an increase from the previous number of 1,679,228.000 Person for 2020. Number of Students: West Bengal: Colleges data is updated yearly, averaging 1,554,327.000 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 1,795,258.000 Person in 2021 and a record low of 609,140.000 Person in 2010. Number of Students: West Bengal: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

Objectives of the analysis

This case study looks at college attending plan. In this case, the government's education department would like to know how many students want to go to college, but high school students may not be willing to share such private information with the education department. So the government needs to predict whether high school students are willing to go to college, and then give instructions to public universities and labor departments. Whether high school students go to college is mainly influenced by themselves and their parents. One way to perform this analysis is to use student data available to the education department. The data in this case study came from a city government that wanted us to be able to use this desensitized student data to make predictions about whether high school students were willing to go to college.

The purpose of this study is to use the basic information of high school students to predict whether they plan to go to college, in particular, which factors can better distinguish high school students who are willing to go to college from those who are not. That could help colleges plan their admissions ahead of time and help the government predict the number of high-school students who will need jobs during graduation season. In data mining terms, we are concerned with classification problems. Therefore, classification algorithms, especially the decision tree algorithm you learned about, can be used for analysis.

Description of the data

Here we will give a brief introduction to the dataset. The data sent to us by the government is in csv format, containing numbers and strings. Students' names are desensitized and replaced by StudentID to prevent information leakage. According to the government official who spoke with us, the data was taken from the actual enrollment data of the city last year, and the last column Plan indicates whether the high school student finally made plans to go to college. The explanatory variables we were able to use included the gender of the student, the IQ of the student, the income of the parents and whether the parents encouraged the child to go to college. Government officials tell us that in their long-term observation, these four factors are the most important factors in whether high school students plan to go to college. The data given to us by government officials is extensive, but analyzing the entire data set is expensive and time-consuming, so we have taken a partial sample for you to analyze. You can get up to 8,000 samples, which is enough to help you train a stable decision tree. The table below details the explanatory variables you can use:

The SATURN database is a database designed for statistical purposes that includes enrolments in non-university higher education (SHU) in the Wallonia-Brussels Federation (FWB). It is based on an annual collection and organized by ARES since 2014. During this collection, each institution transmits its information concerning the registrations of the current academic year as well as the results obtained by the students during the previous academic year. Before using the file, it is important to understand the notion of multiple registration used by the CO_INSMULT variable. A student can have several registrations in the same year, these are differentiated by a registration number. Thus, using this registration number equal to 1, we go from the number of registrations to the number of students. In order to guarantee the maximum anonymity of the persons listed in the database, certain measures have been taken: for higher schools of the arts, the information on the curriculum is limited to the category and field of study for fields informing a country, when there are less than 5 nationals of a country for a year, it is the continent which is indicated for the field indicating the postal code of the legal domicile, when there are less than 5 inhabitants of a municipality for a year, it is the region which is indicated for the field indicating the postal code of the legal domicile, when there are less than 5 inhabitants of a municipality for a year, it is the region which is indicated Finally, from the 2004-2005 academic year to the present, higher education has undergone many changes. For example, the studies included in the category "translation and interpretation" have been transferred from universities to universities and are therefore no longer included here. The same applies to the architecture category provided by higher architectural institutes before being integrated into universities. • Links The studies code makes it possible to make the link with the reference dataset of the studies. • Granularity This is an aggregated dataset. Each row represents a combination of modality for the different variables and an "Account" field indicates the number of entries corresponding to these modalities. • Source The data comes from the "SATURN" database. Log Changes 2021-04-19: correction of the dictionary (cell F183): ND -> Failure (undetermined) 2021-10-06: Updating the structure of the information section and dictionary 2022-08-24: Change the format of the academic year "XXYY" to "20XX-20YY" 2023-30-01: Transfer of the dataset to the ARES portal

Title III-B authorizes the Strengthening Historically Black Colleges and Universities (HBCUs) program award grants to eligible institutions to assist them in strengthening their academic, administrative, and fiscal capabilities. These programs are typically funded through annual discretionary appropriations; and with additional annual mandatory appropriations provided through the FUTURE Act, which extended mandatory funding beyond fiscal year 2019. Funds may be used for the purchase, rental, or lease of scientific or laboratory equipment. Also supported are the construction, maintenance, renovation, and improvement of instruction facilities and physical plant. Funds support faculty exchanges and the development of academic instruction in disciplines in which black Americans are underrepresented. Projects may support the purchase of library materials as well as tutoring, counseling, and STEM programs too. Additionally supported are: funds and administrative management; joint use of facilities; establishment or improvement of development offices; establishment or enhancement of programs of teacher education; establishment of outreach programs; and other activities that a grantee proposes in its application that contribute to carrying out the purposes of the program and are approved by the Secretary as part of the review and acceptance of the grant application.