100+ datasets found
  1. o

    US Colleges and Universities

    • public.opendatasoft.com
    • data.smartidf.services
    csv, excel, geojson +1
    Updated Jun 6, 2025
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    (2025). US Colleges and Universities [Dataset]. https://public.opendatasoft.com/explore/dataset/us-colleges-and-universities/
    Explore at:
    json, excel, geojson, csvAvailable download formats
    Dataset updated
    Jun 6, 2025
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

  2. Data from: College Completion Dataset

    • kaggle.com
    Updated Dec 6, 2022
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    The Devastator (2022). College Completion Dataset [Dataset]. https://www.kaggle.com/datasets/thedevastator/boost-student-success-with-college-completion-da
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 6, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    College Completion Dataset

    Graduation Rates, Race, Efficiency Measures and More

    By Jonathan Ortiz [source]

    About this dataset

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

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

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

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

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

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

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    How to use the dataset

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

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

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

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

  3. n

    School Centroids - Dataset - CKAN

    • nationaldataplatform.org
    Updated Feb 28, 2024
    + more versions
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    (2024). School Centroids - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/school-centroids
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    Dataset updated
    Feb 28, 2024
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The California School Campus Database (CSCD) is now available for all public schools and colleges/universities in California.CSCD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Each is accurately mapped at the assessor parcel level. CSCD is the first statewide database of this information and is available for use without restriction.PURPOSEWhile data is available from the California Department of Education (CDE) at a point level, the data is simplified and often inaccurate.CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data is also provided for community colleges and university lands as of the 2018 release.CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes. It should not be used as the basis for official regulatory, legal, or other such governmental actions unless reviewed by the user and deemed appropriate for their use. See the user manual for more information.Link to California School Campus Database.

  4. College enrollment in public and private institutions in the U.S. 1965-2031

    • statista.com
    • ai-chatbox.pro
    Updated Mar 25, 2025
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    Statista (2025). College enrollment in public and private institutions in the U.S. 1965-2031 [Dataset]. https://www.statista.com/statistics/183995/us-college-enrollment-and-projections-in-public-and-private-institutions/
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    Dataset updated
    Mar 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    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.

  5. o

    US Private Schools

    • public.opendatasoft.com
    • data.smartidf.services
    csv, excel, geojson +1
    Updated Jul 9, 2024
    + more versions
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    (2024). US Private Schools [Dataset]. https://public.opendatasoft.com/explore/dataset/us-private-schools/
    Explore at:
    geojson, json, csv, excelAvailable download formats
    Dataset updated
    Jul 9, 2024
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

  6. QS World

    • kaggle.com
    Updated Jan 27, 2025
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    willian oliveira gibin (2025). QS World [Dataset]. http://doi.org/10.34740/kaggle/dsv/10596910
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 27, 2025
    Dataset provided by
    Kaggle
    Authors
    willian oliveira gibin
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    The QS World University Rankings for 2025 is a list of universities from all over the world, organized to show which ones are the best in various areas. It is widely recognized as one of the most reliable ways to compare higher education institutions. This ranking helps students, researchers, and decision-makers understand how well universities perform in terms of academics, teaching, research, and global connections. Let’s break it down into simple parts so that you can understand it easily.

    What’s in the Ranking? The ranking includes several key pieces of information about each university:

    University Name: This is simply the name of the school. For example, Harvard University or Oxford University. Ranking Position: This tells you the university’s position on the list, like 1st, 50th, or 200th. A lower number means the university is ranked higher. Country/Region: This shows where the university is located, like the USA, the UK, or Japan. Academic Reputation Score: This score is based on surveys of professors and researchers. They give their opinions on which universities are best for studying and learning. Employer Reputation Score: Employers are asked which universities produce the most skilled graduates. This score shows how good a university is at preparing students for jobs. Faculty-Student Ratio: This measures how many students there are per teacher. A lower number means smaller classes and more personal attention for students. Citations per Faculty: This is about research. It shows how often the university’s studies are mentioned in other research papers. The more citations, the better. International Faculty & Students: This looks at how many teachers and students come from different countries, showing how global and diverse the university is. Why Is This Ranking Useful? There are many ways this ranking can help people:

    For Students: It helps students decide where they might want to study. For example, if someone wants a university with a good reputation for teaching and research, they can use this ranking to find the best options. For Universities: Schools can use the rankings to see how they compare to others. If one university is ranked lower than another, it can look at the scores to find ways to improve. For Researchers: Researchers can study the ranking to learn about trends in global education. For example, they might explore why certain regions, like Asia or Europe, have universities that are improving quickly. For Policymakers: Governments and organizations can use the rankings to decide where to invest in education. They can also study which areas of education are most important for the future. What Can We Learn from It? The QS World University Rankings help us learn which universities are leading in academics and research. It also shows us how important global diversity is in education. By understanding these rankings, people can make smarter decisions about studying, teaching, or improving education systems. It’s like a guidebook for the world of universities, helping everyone find the best options and learn from the best practices.

  7. W

    Data from: University lands

    • wifire-data.sdsc.edu
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    csv, esri rest +4
    Updated Jul 18, 2019
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    CA Governor's Office of Emergency Services (2019). University lands [Dataset]. https://wifire-data.sdsc.edu/dataset/university-lands
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    esri rest, kml, csv, html, zip, geojsonAvailable download formats
    Dataset updated
    Jul 18, 2019
    Dataset provided by
    CA Governor's Office of Emergency Services
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description
    The California School Campus Database (CSCD) is now available for all public schools and colleges/universities in California.

    CSCD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Each is accurately mapped at the assessor parcel level. CSCD is the first statewide database of this information and is available for use without restriction.

    PURPOSE
    While data is available from the California Department of Education (CDE) at a point level, the data is simplified and often inaccurate.

    CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.

    The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.

    Data is also provided for community colleges and university lands as of the 2018 release.

    CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes. It should not be used as the basis for official regulatory, legal, or other such governmental actions unless reviewed by the user and deemed appropriate for their use. See the user manual for more information.

  8. d

    College Enrollment, Credit Attainment and Remediation of High School...

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Sep 2, 2023
    + more versions
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    data.ct.gov (2023). College Enrollment, Credit Attainment and Remediation of High School Graduates Statewide [Dataset]. https://catalog.data.gov/dataset/college-enrollment-credit-attainment-and-remediation-of-high-school-graduates-statewide
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    Dataset updated
    Sep 2, 2023
    Dataset provided by
    data.ct.gov
    Description

    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: CT Remed: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

  9. US Highschool students dataset

    • kaggle.com
    zip
    Updated Apr 14, 2024
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    peter mushemi (2024). US Highschool students dataset [Dataset]. https://www.kaggle.com/datasets/petermushemi/us-highschool-students-dataset
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Apr 14, 2024
    Authors
    peter mushemi
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    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.

  10. T

    Public Postsecondary Annual Enrollment

    • educationtocareer.data.mass.gov
    application/rdfxml +5
    Updated May 20, 2024
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    MA Department of Higher Education (2024). Public Postsecondary Annual Enrollment [Dataset]. https://educationtocareer.data.mass.gov/w/j7yp-crt6/default?cur=7o_L7CmPc3y&from=STcs2CGWXKt
    Explore at:
    csv, tsv, application/rssxml, json, xml, application/rdfxmlAvailable download formats
    Dataset updated
    May 20, 2024
    Dataset authored and provided by
    MA Department of Higher Education
    Description

    This dataset contains the total annual FTE and unduplicated headcount enrollment for undergraduate and graduate students at public community colleges and state universities in Massachusetts since 2014.

    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:

    Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

    Related datasets: Public Postsecondary Fall Enrollment 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.

  11. T

    Public Postsecondary Annual Enrollment by Race and Gender

    • educationtocareer.data.mass.gov
    application/rdfxml +5
    Updated May 20, 2024
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    MA Department of Higher Education (2024). Public Postsecondary Annual Enrollment by Race and Gender [Dataset]. https://educationtocareer.data.mass.gov/w/hx2h-9z86/default?cur=sukQLs9Izn8&from=OogqeUivqd3
    Explore at:
    application/rdfxml, json, csv, tsv, xml, application/rssxmlAvailable download formats
    Dataset updated
    May 20, 2024
    Dataset authored and provided by
    MA Department of Higher Education
    Description

    This dataset contains the total annual unduplicated enrollment headcount and percentages by race and gender for undergraduate and graduate students at public community colleges and state universities in Massachusetts since 2014.

    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:

    Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

    Related datasets: Public Postsecondary Fall Enrollment 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.

  12. N

    State College, PA Population Breakdown by Gender and Age Dataset: Male and...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). State College, PA Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/e2026bd7-f25d-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State College, Pennsylvania
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of State College by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for State College. The dataset can be utilized to understand the population distribution of State College by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in State College. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for State College.

    Key observations

    Largest age group (population): Male # 20-24 years (9,133) | Female # 15-19 years (6,378). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Scope of gender :

    Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

    Variables / Data Columns

    • Age Group: This column displays the age group for the State College population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the State College is shown in the following column.
    • Population (Female): The female population in the State College is shown in the following column.
    • Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in State College for each age group.

    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 Gender. You can refer the same here

  13. V

    School Learning Modalities, 2020-2021

    • data.virginia.gov
    • datahub.hhs.gov
    • +4more
    csv, json, rdf, xsl
    Updated Jun 28, 2024
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    Centers for Disease Control and Prevention (2024). School Learning Modalities, 2020-2021 [Dataset]. https://data.virginia.gov/dataset/school-learning-modalities-2020-2021
    Explore at:
    csv, xsl, json, rdfAvailable download formats
    Dataset updated
    Jun 28, 2024
    Dataset provided by
    Centers for Disease Control and Prevention
    Description

    The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

    These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

    School learning modality types are defined as follows:

      • In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
      • Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
      • Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

    Data Information

      • School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
      • You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
      • The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
        • Public school district that is NOT a component of a supervisory union
        • Public school district that is a component of a supervisory union
        • Independent charter district
      • “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

    Technical Notes

      • Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
      • Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

    Sources

      1. K-12 School Opening Tracker. Burbio 2021; https

  14. Data from: Understanding Crime Victimization Among College Students in the...

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated Mar 12, 2025
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    National Institute of Justice (2025). Understanding Crime Victimization Among College Students in the United States, 1993-1994 [Dataset]. https://catalog.data.gov/dataset/understanding-crime-victimization-among-college-students-in-the-united-states-1993-1994-8afc5
    Explore at:
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    United States
    Description

    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.

  15. State Private Universities of India

    • kaggle.com
    Updated Jan 25, 2024
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    Shri Ganesh Maniram Birbanwale (2024). State Private Universities of India [Dataset]. http://doi.org/10.34740/kaggle/dsv/7481136
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 25, 2024
    Dataset provided by
    Kaggle
    Authors
    Shri Ganesh Maniram Birbanwale
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    India
    Description

    n the ever-evolving landscape of higher education, predicting future trends in engineering private universities is essential for adapting to changing demands and staying competitive. Several factors contribute to shaping the future of these institutions.

    1. Technology Integration: The increasing reliance on technology in various industries will likely drive engineering private universities to integrate emerging technologies into their curricula. Predictions may include a surge in programs related to artificial intelligence, cybersecurity, and sustainable technologies.

    2. Industry Partnerships: Anticipating a closer collaboration between engineering private universities and industries can be a key prediction. These partnerships may lead to specialized programs, research opportunities, and real-world projects for students.

    3. Globalization and Diversity: As the world becomes more interconnected, engineering private universities may witness a rise in diversity among both students and faculty. Predicting increased international collaborations, exchange programs, and a multicultural learning environment could be part of the trend.

    4. Online and Blended Learning: The future may see a continued growth in online and blended learning options, allowing engineering private universities to reach a broader audience. Predictions might include the expansion of virtual labs, online degree programs, and innovative educational technologies.

    5. Focus on Soft Skills: Beyond technical expertise, employers increasingly value soft skills. Predictions may involve engineering private universities placing a stronger emphasis on communication, teamwork, and problem-solving skills in their educational programs.

    Conclusion: While predicting the future is inherently uncertain, these trends in technology, collaboration, diversity, online learning, and soft skills can serve as a foundation for anticipating the direction of engineering private universities. Staying adaptable and responsive to the evolving needs of students and industries will be crucial for these institutions.

  16. World university rankings by Times Higher Education 2024/2025

    • statista.com
    • ai-chatbox.pro
    Updated Jun 5, 2025
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    Statista (2025). World university rankings by Times Higher Education 2024/2025 [Dataset]. https://www.statista.com/statistics/226681/world-university-rankings-by-times-higher-education/
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    Dataset updated
    Jun 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Worldwide
    Description

    For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

  17. N

    State College, PA Population Breakdown by Gender and Age Dataset: Male and...

    • neilsberg.com
    csv, json
    Updated Feb 19, 2024
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    Neilsberg Research (2024). State College, PA Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/8e6e8cf7-c989-11ee-9145-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 19, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State College, Pennsylvania
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of State College by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for State College. The dataset can be utilized to understand the population distribution of State College by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in State College. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for State College.

    Key observations

    Largest age group (population): Male # 20-24 years (9,348) | Female # 20-24 years (7,262). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Scope of gender :

    Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

    Variables / Data Columns

    • Age Group: This column displays the age group for the State College population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the State College is shown in the following column.
    • Population (Female): The female population in the State College is shown in the following column.
    • Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in State College for each age group.

    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 Gender. You can refer the same here

  18. d

    Allegheny County Private Schools Locations

    • catalog.data.gov
    • data.wprdc.org
    • +2more
    Updated May 14, 2023
    + more versions
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    Allegheny County (2023). Allegheny County Private Schools Locations [Dataset]. https://catalog.data.gov/dataset/allegheny-county-private-schools-locations
    Explore at:
    Dataset updated
    May 14, 2023
    Dataset provided by
    Allegheny County
    Area covered
    Allegheny County
    Description

    These geocoded locations are based on the Allegheny County extract of Educational Names & Addresses (EdNA) via Pennsylvania Department of Education website as of April 19, 2018. Several addresses were not able to be geocoded (ex. If PO Box addresses were provided, they were not geocoded.)If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Education Organization: Allegheny County Department: Department of Human Services Temporal Coverage: as of April 19, 2018 Data Notes: Coordinate System: GCS_North_American_1983 Development Notes: none Other: none Related Document(s): Data Dictionary - none Frequency - Data Change: April, 19, 2018 data Frequency - Publishing: one-time Data Steward Name: See http://www.edna.ed.state.pa.us/Screens/Extracts/wfExtractEntitiesAdmin.aspx for more information. Data Steward Email: RA-DDQDataCollection@pa.gov (Data Collection Team)

  19. a

    California School Campus Database 2018

    • hub.arcgis.com
    • gis-calema.opendata.arcgis.com
    Updated Jul 18, 2019
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    CA Governor's Office of Emergency Services (2019). California School Campus Database 2018 [Dataset]. https://hub.arcgis.com/maps/494bae27c34743678413e03170917f22
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    Dataset updated
    Jul 18, 2019
    Dataset authored and provided by
    CA Governor's Office of Emergency Services
    Area covered
    Description

    The California School Campus Database (CSCD) is now available for all public schools and colleges/universities in California.CSCD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Each is accurately mapped at the assessor parcel level. CSCD is the first statewide database of this information and is available for use without restriction.PURPOSEWhile data is available from the California Department of Education (CDE) at a point level, the data is simplified and often inaccurate.CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data is also provided for community colleges and university lands as of the 2018 release.CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes. It should not be used as the basis for official regulatory, legal, or other such governmental actions unless reviewed by the user and deemed appropriate for their use. See the user manual for more information.Link to California School Campus Database.

  20. Public School Characteristics - Current

    • catalog.data.gov
    • datasets.ai
    • +3more
    Updated Oct 21, 2024
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    National Center for Education Statistics (NCES) (2024). Public School Characteristics - Current [Dataset]. https://catalog.data.gov/dataset/public-school-characteristics-current-340b1
    Explore at:
    Dataset updated
    Oct 21, 2024
    Dataset provided by
    National Center for Education Statisticshttps://nces.ed.gov/
    Description

    The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. Collections are available for the following years:

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(2025). US Colleges and Universities [Dataset]. https://public.opendatasoft.com/explore/dataset/us-colleges-and-universities/

US Colleges and Universities

Explore at:
json, excel, geojson, csvAvailable download formats
Dataset updated
Jun 6, 2025
License

https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

Area covered
United States
Description

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

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