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.

Graph and download economic data for Bachelor's Degree or Higher (5-year estimate) in Oklahoma County, OK (HC01ESTVC1740109) from 2010 to 2023 about Oklahoma County, OK; Oklahoma City; OK; tertiary schooling; educational attainment; education; 5-year; and USA.

In 2021, San Francisco Bay, CA had the highest startup educational strength total score of any city in the United States at 99.65. The total startup score is a sum of quantity, quality, and business environment evaluations that determines the best ecosystem in United States according to their strength in education. New York City, NY had the second highest score, at 22.3 in 2021.

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.

Graph and download economic data for High School Graduate or Higher (5-year estimate) in Mohave County, AZ (HC01ESTVC1604015) from 2010 to 2023 about Mohave County, AZ; Lake Havasu City; secondary schooling; secondary; AZ; educational attainment; education; 5-year; and USA.

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 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 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 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 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

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.

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.

Graph and download economic data for High School Graduate or Higher (5-year estimate) in LaPorte County, IN (HC01ESTVC1618091) from 2010 to 2023 about La Porte County, IN; Michigan City; secondary schooling; secondary; educational attainment; IN; education; 5-year; and USA.

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

Graph and download economic data for High School Graduate or Higher (5-year estimate) in Philadelphia County/city, PA (HC01ESTVC1642101) from 2010 to 2023 about Philadelphia County/City, PA; Philadelphia; secondary schooling; secondary; educational attainment; PA; education; 5-year; and USA.

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

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.

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...

Public schools have managed to maintain revenue growth despite significant shifts in funding, enrollment and parental preferences. Class sizes are shrinking every year as birth rates drop and the high school retention rate stagnates, straining revenue as smaller schools see lessened funding from governments. Public schools have contended with heightened competition from alternative education options, especially homeschooling and private institutions, as parents seek more personalized educational experiences. States have increasingly adopted school choice systems, allowing parents to use public funds or tax credits to pay for private schooling. The Trump administration has taken steps to promote these programs even more and has proposed establishing a federal voucher system. Despite heightened competition and a rigorous competitive atmosphere, strong per-pupil funding amid strong state and local budgets has buoyed public schools. Public schools' revenue has been climbing at a CAGR of 1.4% to an estimated $1.0 billion over the five years through 2025, including a rise of 0.9% in 2025 alone. Governments fully fund public schools. Support from state and local governments is especially vital, as they provide nearly nine-tenths of public schools' revenue. Despite a slight dip in 2022, strong tax income pushed up government funding for primary and secondary schools by 6.2% in 2023. These resources are enabling public schools to invest in tutoring and counseling to improve their educational outcomes and better compete with alternative primary and secondary schools. Public schools also used funds to help transition to online and augmented education and have avoided taking on further losses as shrinking class sizes leave them without pressure to continue purchasing new laptops or tablets. Still, public schools are not profitable and largely operate at a loss every year. Public schools are set to face a continued drop in enrollment as well as intensifying competition. To sustain revenue and support, schools will focus on retaining students and improving academic outcomes despite potential federal funding changes. The expansion of school choice programs will compel public schools to enhance their quality and offer additional services like after-school programs to sustain enrollment and win parental support as families gain more access to private schools. Still, charter schools will leverage their unique value propositions to remain competitive and buoy enrollment in the public school system. Public schools' revenue is set to stagnate, swelling at a CAGR of just 0.2% to an estimated $1.0 billion through the end of 2030.