This statistic shows the share of renters who chose city for job vs. location in the United States in 2018, by education level. In 2018, 64.5 percent of non-student renters without a Bachelor's degree were more likely to choose a city for the location itself, whereas 62.4 percent of STEM graduates were more likely to move to a new city for a job rather than the city itself.

This statistic shows the top metropolitan areas with the highest percentage of college graduates in the United States in 2019. In 2019, Boulder in Colorado was ranked first with 64.8 percent of its population having a Bachelor's degree or higher.

This statistic shows the share of degree holders among the population aged 25 years and older in the ten cities Sungard Availability Services considered to be the best for tech start-ups in the United States. Seattle had the highest rate of bachelor's degree holders at 58.9 percent, while Washington had the highest rate of advanced degree holders at 30.6 percent.

Table from the American Community Survey (ACS) 5-year series on education enrollment and attainment related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B14007/B14002 School Enrollment, B15003 Educational Attainment. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B14007, B15003, B14002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

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!

Preliminary Class Size Report School K-8 Average Class Size General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students.

2018-19 Preliminary Class Size Report School K-8 Average Class Size

General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

This table contains data on educational attainment from the American Community Survey 2006-2010 database for counties. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10EDUCNTYMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

IntroductionWe aimed to examine utilitarian bicycle use among adults from 18 large Latin American cities and its association with socio-economic position (education and income) between 2008 and 2018.MethodsData came from yearly cross-sectional surveys collected by the Development Bank of Latin America (CAF). A total of 77,765 survey respondents with complete data were used to estimate multilevel logistic regression models with city as random intercept and year as random slope.ResultsIndividuals with high education and high-income levels had lower odds of using a bicycle compared with participants with lower education and income levels. These associations, however, changed over time with the odds of bicycle use increasing for all groups, especially among individuals with the highest education and income levels.DiscussionOur results confirm the broadening appeal of bicycling across socio-economic positions in several Latin American cities and reinforce the importance of considering policies aimed at supporting and enhancing bicycle travel for all users.

2018-19 Preliminary Class Size Report City K-8 Class Size Distribution

General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students

2018-19 Preliminary Class Size Report City Middle and High School Class Size Distribution Core courses identified as English, Math, Social Studies, and Science classes for grades 6-12, where available General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students

Preliminary Class Size Report Middle School and High School Core Average Class Size Core courses identified as English, Math, Social Studies, and Science classes for grades 6-12, where available in the scheduling application. General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students.

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

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How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

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](https://creativecommons.org/public...

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores.

Records with 5 or fewer students are suppressed (marked ‘s’).

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

Data collected and processed by the College Board.

This dataset tracks annual american indian student percentage from 2013 to 2023 for Muskegon Community Education Center vs. Michigan and Muskegon Public Schools Of The City Of School District

This feature service contains data from the American Community Survey: 5-year Estimates Subject Tables for the greater Bozeman, MT area. The attributes come from the Educational Attainment table (S1501). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases.For Example: PctPop_45to64_HS is equal to the percentage of the population ages 45 to 64 with the educational attainment of a high school degree or equivalentData DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyPctPop: Percent of the selected populationRace/Ethnicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesAge Group:18to24: Ages 18 to 24 years old25to34: Ages 25 to 34 years old35to44: Ages 35 to 44 years old45to64: Ages 45 to 64 years old65andover: Ages 65 and overEducational AttainmentBA: Bachelor's degree or higherHS: High school graduate (includes equivalency)Download ACS Educational Attainment data for the greater Bozeman, MT areaAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

2018-19 Preliminary Class Size Report City Middle School and High School Core Average Class Size Core courses identified as English, Math, Social Studies, and Science classes for grades 6-12, where available General Education and Integrated Co-Teaching (ICT) classes and course sections with more than 100 students or fewer than seven students are excluded from this report, as are self-contained courses with fewer than two students

This layer shows education levels. Counts are broken down by sex. Data is from US Census American Community Survey (ACS) 5-year estimates.Data shown in this layer is a percentage of total households.This layer is symbolized by the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2019-2023ACS Table(s): B15002 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 12, 2024National Figures: data.census.gov

This dataset tracks annual american indian student percentage from 1999 to 2023 for Alternative Family Education vs. California and Santa Cruz City High School District