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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

Higher Education Institutions in the United States of America Dataset

This repository contains a dataset of higher education institutions in the United States of America. This dataset was compiled in response to a cybersecurity research of American higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

Data

The data includes the following fields for each institution:

• Id: A unique identifier assigned to each institution.
• Region: The federal state in which the institution is located.
• Name: The full name of the institution.
• Category: Indicates whether the institution is public or private.
• Url: The website of the institution.

Methodology

The dataset was obtained from the Higher Education Integrated Data System (IPEDS) website [3], which is administered by the National Center for Education Statistics (NCES). NCES serves as the primary federal entity for collecting and analyzing education-related data in the United States. The data was collected on February 2, 2023.

The initial list of institutions was derived from the IPEDS database using the following criteria: (1) US institutions only, (2) degree-granting institutions, primarily bachelor's or higher, and (3) industry classification, which includes: public 4 - year or above, private not-for-profit 4 years or more, private for-profit 4 years or more, public 2 years, private not-for-profit 2 years, private for-profit 2 years, public less than 2 years, private not-for-profit for-profit less than 2 years and private for-profit less than 2 years.

The following variables have been added to the list of institutions: Control of the institution, state abbreviation, degree-granting status, Status of the institution, and Institution's internet website address. This resulted in a report with 1,979 institutions.

The institution's status was labeled with the following values: A (Active), N (New), R (Restored), M (Closed in the current year), C (Combined with another institution), D (Deleted out of business), I (Inactive due to hurricane-related issues), O (Outside IPEDS scope), P (Potential new/add institution), Q (Potential institution reestablishment), W (Potential addition outside IPEDS scope), X ( Potential restoration outside the scope of IPEDS) and G (Perfect Children's Campus).

A filter was applied to the report to retain only institutions with an A, N, or R status, resulting in 1,978 institutions. Finally, a data cleaning process was applied, which involved removing the whitespace at the beginning and end of cell content and duplicate whitespace. The final data were compiled into the dataset included in this repository.

Usage

This data is available under the Creative Commons Zero (CC0) license and can be used for any purpose, including academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.7614862

Contribution

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

Acknowledgment

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Instituto Politécnico de Viana do Castelo, Portugal.

References

1. Pending.
2. S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]
3. Integrated Postsecondary Education Data System, "Compare Institutions", Fev 2023. [online]. Available: https://nces.ed.gov/ipeds/use-the-data

In an impressive increase from years past, 40.1 percent of women in the United States had completed four years or more of college in 2024. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 37.1 percent of the U.S. male population having completed four years or more of college in 2024, up from 5.5 percent in 1940. 4- and 2-year colleges In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree. Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job. Earnings after college Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance. All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

This dataset presents student information from a General Degree College, where subjects are selected according to high school performance. Included are categories, gender, year of passing, marks for the first choice subject, the first choice subject itself, marks for the second choice subject, and subsequent choices. 📊 Ideal for in-depth data analysis in Excel, this dataset offers insights into academic preferences and trends. Let's dive in and craft a compelling dashboard to unlock its full potential! 🚀

This dataset contains information about different colleges and their programs. Here is a breakdown of the columns in the dataset:

college_name: The name of the college or institution. college_link: The URL link to the college's website or relevant information source. duration: The duration of the program offered by the college, typically in years. fees: The fees or cost of the program in Indian Rupees (INR). rank: The ranking of the college or program (if available). placement: Placement-related information, such as the average and highest salary offered to graduates. c_type: The type of the college, which could indicate whether it is autonomous or affiliated with a university. program: The program or degree offered by the college, such as Bachelor of Technology (B.Tech).

As part of the American Community Survey (ACS), the U.S. Census Bureau collects information regarding respondents' educational attainment. Educational attainment refers to the highest level of education that all individuals age 25 and older have completed. Response categories include no schooling completed; nursery school, grades 1 through 11; 12th grade but no diploma; regular high school diploma; GED or alternative credential; some college credit, but less than one year of college; one or more years of college credit, no degree; associate's degree; bachelor's degree; master's degree, professional degree beyond bachelor's degree; and doctorate degree. Data from the 2000 Decennial Census is also summarized.

Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Private and Public university's acceptance dataset. A data frame with 777 observations on the following 18 variables. it's good for practicing cluster analysis, data visualization, management, analysis, and predictions.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

This dataset provides data on the number of students enrolled in public colleges and universities in Qatar, categorized by nationality, country of origin, and gender. The dataset includes students from the Gulf Cooperation Council (G.C.C.) countries, such as Qatar, the United Arab Emirates, Bahrain, Kuwait, Saudi Arabia, and Oman, as well as students from other Arab countries, such as Iraq. This dataset helps in understanding the distribution of students from different countries and genders in Qatar’s higher education institutions.

Further information about the study may be found on the COVID Social Mobility and Opportunities Study (COSMO) webpage.

COSMO Wave 2, 2022-2023
All young people who took part in Wave 1 (see SN 9000) were invited to the second Wave of the study, along with their parents (whether or not they took part in Wave 1).

Data collection in Wave 2 was carried out between October 2022 and April 2023 where young people and parents/guardians were first invited to a web survey. In addition to online reminders, some non-respondents were followed up via face-to-face visits or telephone calls over the winter and throughout spring. Online ‘mop-up’ fieldwork was also carried out to invite all non-respondents into the survey one last time before the end of fieldwork.

Latest edition information:
For the second edition (April 2024), a standalone dataset from the Keeping in Touch (KIT) exercise carried out after the completion of Wave 2, late 2023 have been deposited. This entailed a very short questionnaire for updating contact details and brief updates on young people's lives. A longitudinal parents dataset has also been deposited, to help data users find core background information from parents who took part in either Wave 1 or Wave 2 in one place. Finally, the young people's dataset has been updated (version 1.1) with additional codes added from some open-ended questions. The COSMO Wave 1 Data User Guide Version 1.1 explains these updates in detail. A technical report and accompanying appendices has also been deposited.

Further information about the study may be found on the COSMO website.

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!

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES SCHOOL ENROLLMENT - DP02 Universe - Population 3 Year and over enrolled in school Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

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 contains information on college graduates who graduated between 2010 and 2012. This data was collected by American Community Survey 2010-2012 Public Use Microdata Series and then uploaded to GitHub by Randy Olsen here. I reuploaded the dataset here on Kaggle to demonstrate data visualization techniques.

A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

US State Education Analysis for Tech Product Launch

About This Dataset

This comprehensive dataset provides detailed educational attainment and demographic analysis across all 50 US states from 2021-2023, specifically designed for tech companies planning strategic market entry and product launch decisions.

Dataset Overview

• 150 rows of data (50 states × 3 years)
• 17 columns of educational, demographic, and economic indicators
• Complete coverage of all US states from 2021-2023
• Ready-to-analyze format with calculated percentages and rankings

Key Features

🎯 Strategic Market Intelligence

• Educational attainment levels by degree type (Bachelor's, Master's, Professional, Doctoral)
• Calculated education scores and state rankings for quick market prioritization
• Median household income data for purchasing power assessment

📊 Comprehensive Demographics

• Population data for adults 25+ (primary tech consumer demographic)
• Household count data for market sizing
• College graduate percentages for targeted marketing

🔢 Advanced Analytics Ready

• Pre-calculated composite education scores
• State rankings based on education levels
• Percentage breakdowns for immediate insights

Column Definitions

Use Cases

🚀 Tech Product Launches

• Identify states with highest concentrations of educated early adopters
• Prioritize markets based on education levels and income
• Size potential customer segments by state

📈 Market Research & Analysis

• Compare educational demographics across regions
• Analyze trends in educational attainment over time
• Correlate education levels with income potential

🎯 Customer Segmentation

• Target high-value customer segments (graduate degree holders)
• Develop region-specific marketing strategies
• Plan B2B tech sales territories

📊 Business Intelligence

• Regional expansion planning
• Competitive market analysis
• Investment and resource allocation decisions

Data Quality & Sources

• Primary Sources: US Census Bureau American Community Survey (ACS), Bureau of Labor Statistics
• Data Validation: Cross-referenced against multiple official sources
• Calculation Methodology: All percentages and scores calculated using consistent formulas
• Update Frequency: Annual updates as new official data becomes available

Sample Insights

The dataset reveals that Massachusetts consistently ranks #1 in education metrics with: - 47.78% college graduation rate (2023) - 22.42% graduate degree holders - $99,858 median household income - Education score of 28.76

Perfect for identifying premium tech markets and highly-educated consumer bases for sophisticated technology products.

This dataset is ideal for data scientists, market researchers, business analysts, and tech companies looking to make data-driven decisions about market entry, customer targeting, and regional strategy.

This dataset contains the most recent cohorts' data from the U.S. Department of Education's College Scorecard, providing detailed insights into U.S. higher education institutions and their graduates. It includes two primary files: one focusing on data by field of study and the other on institution-level data. This dataset is ideal for researchers, educators, and policymakers interested in recent trends and outcomes in higher education.

Files Included: 1. Most-Recent-Cohorts-Field-of-Study.csv (149.67 MB) - Contains data on recent graduates by field of study. - Includes information on cumulative debt at graduation and earnings one year after graduation.

1. Most-Recent-Cohorts-Institution.csv (104.14 MB)
   • Contains institution-level data on recent cohorts.
   • Includes information on institutional characteristics, enrollment, student aid, costs, and student outcomes.

Key Features: - Detailed breakdown by field of study and institution. - Recent data on student debt and earnings. - Insights into institutional performance and student outcomes.

Usage: - Analyze recent trends in higher education. - Compare outcomes across different fields of study. - Research institutional performance and characteristics.

Data Source: U.S. Department of Education College Scorecard, last updated June 13, 2024.

Licensing: This dataset is provided under the Public Domain Dedication and License (PDDL).

Citation: U.S. Department of Education, College Scorecard Recent Cohorts Data.