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 data here is from the report entitled Trends in Enrollment, Credit Attainment, and Remediation at Connecticut Public Universities and Community Colleges: Results from P20WIN for the High School Graduating Classes of 2010 through 2016.

The report answers three questions: 1. Enrollment: What percentage of the graduating class enrolled in a Connecticut public university or community college (UCONN, the four Connecticut State Universities, and 12 Connecticut community colleges) within 16 months of graduation? 2. Credit Attainment: What percentage of those who enrolled in a Connecticut public university or community college within 16 months of graduation earned at least one year’s worth of credits (24 or more) within two years of enrollment? 3. Remediation: What percentage of those who enrolled in one of the four Connecticut State Universities or one of the 12 community colleges within 16 months of graduation took a remedial course within two years of enrollment?

Notes on the data: School Credit: % Earning 24 Credits is a subset of the % Enrolled in 16 Months. School Remediation: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

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.

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

Graph and download economic data for Unemployment Rate - College Graduates - Professional Degree, 25 to 34 years (CGPD2534) from Jan 2000 to Aug 2025 about professional degree, 25 to 34 years, professional, tertiary schooling, education, unemployment, rate, and USA.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

In the 2021/22 academic year, about 4.1 million higher education degrees were earned in the United States. By the 2031/32 academic year, this figure is projected to increase to about 4.83 million degrees.

Data product is provided by ASL Marketing. It contains current college students who are attending colleges and universities nationwide. Connect with this market by: Class Year Field of Study Home/School address College Attending Ethnicity School Type Region Sports Conference Gender eSports Email

See notice below about this dataset

This dataset provides the number of graduates who enrolled in each type of postsecondary education per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Outcomes

• Total Postsecondary Enrollment
• In-State Public 2-Year
• In-State Public 4-Year
• In-State Private
• Out-of-State
• Total Employed
• Total Missing

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Content

A data frame with 777 observations on the following 18 variables.

Private A factor with levels No and Yes indicating private or public university

Apps Number of applications received

Accept Number of applications accepted

Enroll Number of new students enrolled

Top10perc Pct. new students from top 10% of H.S. class

Top25perc Pct. new students from top 25% of H.S. class

F.Undergrad Number of fulltime undergraduates

P.Undergrad Number of parttime undergraduates

Outstate Out-of-state tuition

Room.Board Room and board costs

Books Estimated book costs

Personal Estimated personal spending

PhD Pct. of faculty with Ph.D.’s

Terminal Pct. of faculty with terminal degree

S.F.Ratio Student/faculty ratio

perc.alumni Pct. alumni who donate

Expend Instructional expenditure per student

Grad.Rate Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.

--- Original source retains full ownership of the source dataset ---

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document skill development in higher education at a similar granularity.Here, we fill this gap by presenting a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions.To construct this dataset, we apply natural language processing to extract from course descriptions detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these DWAs to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college-educated workers and their role in the economy.To showcase the utility of this dataset, we use it to 1) compare the similarity of skills taught and skills in the workforce according to the US Bureau of Labor Statistics, 2) estimate gender differences in acquired skills based on enrollment data, 3) depict temporal trends in the skills taught in social science curricula, and 4) connect college majors' skill distinctiveness to salary differences of graduates.Overall, this dataset can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands especially in the face of new technologies.Citation:The preprint is available here:https://arxiv.org/abs/2404.13163 If you are using the dataset, please use the following citation:@article{sabet2024national, title={A national longitudinal dataset of skills taught in US higher education curricula}, author={Sabet, Alireza Javadian and Bana, Sarah H and Yu, Renzhe and Frank, Morgan R}, journal={arXiv preprint arXiv:2404.13163}, year={2024}}

This dataset explore the Residence and migration of all freshmen students in degree-granting institutions who graduated from high school in the previous 12 months, by state: Fall 2004 NOTE: Includes all first-time postsecondary students enrolled at reporting institutions. Degree-granting institutions grant associate's or higher degrees and participate in Title IV federal financial aid programs. SOURCE: U.S. Department of Education, National Center for Education Statistics, Integrated Postsecondary Education Data System (IPEDS), Spring 2005. (This table was prepared September 2005.) http://nces.ed.gov/programs/digest/d06/tables/dt06_208.asp Accessed on 12 November 2007

The International STEM Graduate Student Survey assesses why international students are coming to the United States for their graduate studies, the challenges they have faced while studying in the US, their future career plans, and whether they wish to stay or leave the US upon graduation. According to the Survey of Earned Doctorates by the National Science Foundation and the National Center for Science and Engineering Statistics, international students accounted for over 40% of all US doctoral graduates in STEM in 2013. The factors that influence international students' decisions to study in the US and whether they will stay or leave are important to US economic competitiveness. We contacted graduate students (both domestic and international) in STEM disciplines from the top 10 universities ranked by the total number of enrolled international students. We estimate that we contacted approximately 15,990 students. Individuals were asked to taken an online survey regarding their background, reasons for studying in the US, and whether they plan to stay or leave the US upon graduation. We received a total of 2,322 completed surveys, giving us a response rate of 14.5%. 1,535 of the completed were from domestic students and 787 of which were from international students. Raw survey data are presented here.Survey participants were contacted via Qualtrics to participate in this survey. The Universe of this survey data set pertains to all graduate students (Master's and PhD) in STEM disciplines from the following universities: Columbia University, University of Illinois-Urbana Champaign, Michigan State University, Northeastern University, Purdue University, University of Southern California, Arizona State University, University of California at Los Angeles, New York University, University of Washington at Seattle. Data are broken into 2 subsets: one for international STEM graduate students and one for domestic STEM graduate students, please see respective files.

This data collection contains enrollment information from all institutions of higher education in the United States in 1976. The data include student enrollment classified by type (i.e., enrolled in bachelors degree programs, enrolled in vocational-technical programs, first-time students, and new transfer students, non-classified students, first-professional students, and graduate students), residence status (e.g., in-state, out-of-state, or foreign), by home state or country, and by sex. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07646.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The Institutional Data Archive on American Higher Education (IDA) contains academic data on 384 four-year colleges and universities in the United States. The IDA is one of two databases produced by the Colleges and Universities 2000 project based at the University of California, Riverside. This release, the third compilation of the IDA, is updated through academic year 2010-2011, and includes longitudinal and cross-sectional data from multiple sources. The collection is organized into nine datasets based on the unit of analysis and whether identifiers linking the data to particular institutions are present; seven of the datasets can be linked by a common identifier variable (PROJ_ID), and two cannot be linked due to confidentiality agreements. The seven identifiable datasets contain information on institutions, university systems, programs and academic departments, earned degrees, graduate schools, medical schools, and institutional academic rankings over time. Data regarding student enrollments, average SAT and ACT scores, and tuition and fees has been recorded, as well as institutional information concerning libraries, research activity, revenue and expenditures, faculty salaries, and quality rankings for program faculty. The identifiable datasets also include census information for neighborhoods surrounding IDA colleges and universities. The two non-identifiable datasets contain confidential survey responses from IDA institution presidents, chancellors, provosts, and academic vice presidents; survey questions pertained to governance structures, institutional goals and achievements, and solicited opinions on current and future issues facing the respondent's institution and higher education in general.

This study was designed to collect college student victimization data to satisfy four primary objectives: (1) to determine the prevalence and nature of campus crime, (2) to help the campus community more fully assess crime, perceived risk, fear of victimization, and security problems, (3) to aid in the development and evaluation of location-specific and campus-wide security policies and crime prevention measures, and (4) to make a contribution to the theoretical study of campus crime and security. Data for Part 1, Student-Level Data, and Part 2, Incident-Level Data, were collected from a random sample of college students in the United States using a structured telephone interview modeled after the redesigned National Crime Victimization Survey administered by the Bureau of Justice Statistics. Using stratified random sampling, over 3,000 college students from 12 schools were interviewed. Researchers collected detailed information about the incident and the victimization, and demographic characteristics of victims and nonvictims, as well as data on self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 3, School Data, the researchers surveyed campus officials at the sampled schools and gathered official data to supplement institution-level crime prevention information obtained from the students. Mail-back surveys were sent to directors of campus security or campus police at the 12 sampled schools, addressing various aspects of campus security, crime prevention programs, and crime prevention services available on the campuses. Additionally, mail-back surveys were sent to directors of campus planning, facilities management, or related offices at the same 12 schools to obtain information on the extent and type of planning and design actions taken by the campus for crime prevention. Part 3 also contains data on the characteristics of the 12 schools obtained from PETERSON'S GUIDE TO FOUR-YEAR COLLEGES (1994). Part 4, Census Data, is comprised of 1990 Census data describing the census tracts in which the 12 schools were located and all tracts adjacent to the schools. Demographic variables in Part 1 include year of birth, sex, race, marital status, current enrollment status, employment status, residency status, and parents' education. Victimization variables include whether the student had ever been a victim of theft, burglary, robbery, motor vehicle theft, assault, sexual assault, vandalism, or harassment. Students who had been victimized were also asked the number of times victimization incidents occurred, how often the police were called, and if they knew the perpetrator. All students were asked about measures of self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 2, questions were asked about the location of each incident, whether the offender had a weapon, a description of the offense and the victim's response, injuries incurred, characteristics of the offender, and whether the incident was reported to the police. For Part 3, respondents were asked about how general campus security needs were met, the nature and extent of crime prevention programs and services available at the school (including when the program or service was first implemented), and recent crime prevention activities. Campus planners were asked if specific types of campus security features (e.g., emergency telephone, territorial markers, perimeter barriers, key-card access, surveillance cameras, crime safety audits, design review for safety features, trimming shrubs and underbrush to reduce hiding places, etc.) were present during the 1993-1994 academic year and if yes, how many or how often. Additionally, data were collected on total full-time enrollment, type of institution, percent of undergraduate female students enrolled, percent of African-American students enrolled, acreage, total fraternities, total sororities, crime rate of city/county where the school was located, and the school's Carnegie classification. For Part 4, Census data were compiled on percent unemployed, percent having a high school degree or higher, percent of all persons below the poverty level, and percent of the population that was Black.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

Alesco's college and alumni data contains demographic information on almost every college student in the nation. Nowhere else will you find more complete and accurate information on student and alumni, individuals by name and age and career interests along with detailed financial-related data including income.

Our student data is built by utilizing hundreds of sources including public records, directories, county recorder and tax assessor files, US Census data, surveys, and purchase transactions. We continuously utilize USPS processing routines to give you the most complete and up-to-date addresses.

Flexible pricing available to meet all your business needs. Student Data is available on a transactional basis or unlimited use cases for marketing and analytics.

Student Marketing Data Education Marketing Data Alumni Data

This dataset shows the percentage of students who graduated from Massachusetts public schools with a regular high school diploma within 4 or 5 years. It is a long file that contains multiple rows for each school and district, with rows for different years and different student groups.

Note: Data is currently available at the school level only, as well as the state overall. For district-level graduation rates, please see the High School Graduation Rates dataset, or the High School Graduation Rates report on our DESE Profiles site.

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. 

For more data about student experiences and outcomes in high school and beyond, please see the main DART: Success After High School dataset and dashboard.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce.While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.