The best master's degree for getting a job was considered to be Physicians Assistant with a mid-career median salary of ****** U.S. dollars in 2021. Salaries for nurse practitioner and computer science master's were also high.

In the academic year of 2022, it is expected that 551,460 female and 331,530 male students will earn a Master’s degree in the United States. These figures are a significant increase from the academic year of 1950, when 16,980 female students and 41,220 male students earned a Master’s degree.

What is a Master’s degree?

A Master’s degree is an academic degree granted by universities after finishing a Bachelor’s degree. Master’s degrees focus in on a specific field and are more specialized than a Bachelor’s. A typical Master’s program is about two years long, with the final semester focusing on the thesis. Master’s degree programs are usually harder to get into than Bachelor’s degree programs, due to the rigor of the program. Because these programs are so competitive, those with a Master’s degree are typically paid more than those with a Bachelor’s degree.

Master’s degrees in the United States

The number of master’s degrees granted in the United States has steadily increased since the 1970s and is expected to continue to increase. In 2021, the Master’s degree program with the worst job prospects in the United States by mid-career median pay was counseling, while the program with the best job prospects was a physician's assistant.

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.

These data were collected as part of the evaluation of the Healthy School Program (HSP), a program that provides support to elementary, middle, and high schools in the United States as they work to create healthy school environments that promote physical activity and healthy eating for students and staff. HSP was created in 2006 by the Alliance for a Healthier Generation with funding from the Robert Wood Johnson Foundation. The HSP evaluation addressed both process and impact outcomes: Is the HSP technical assistance and training model effective in increasing the implementation of policies and programs that promote and provide access to healthier foods and more physical activity before, during and after school? Are there distinctive or common school-level characteristics that hasten or hinder school-level implementation of policies and programs that promote and provide access to healthy foods and physical activity in the school setting in HSP schools? Does participation in HSP contribute to an increase in healthy eating behaviors and physical activity participation among students? Does participation in HSP contribute to a decrease in body mass index (BMI) among students? The evaluation used a mixed-method design incorporating both quantitative and qualitative components. The quantitative component of the evaluation was a longitudinal design that measured student changes in eating and physical activity behaviors and BMI and schools' implementation of policies and practices promoted by HSP. For the qualitative component the evaluation team conducted site visits in a sample of HSP schools. Nine data files constitute this data collection: HSP Participation and Inventory Data File, 2006-2011 (originally called the Inventory Data File) Pilot Student Survey Data File Pilot Student Height and Weight Measurements Data File Survey of Students in Boston and Miami-Dade Public Schools Data File HSP Participation and Inventory Data File, 2006-2014 Arizona, Prince George's County and Nevada Healthy Schools Youth Survey Data File Arizona and Prince George's County Youth Height and Weight Measurements Data File Arizona Academic Achievement Data File Prince George's County School Wellness Coordinator Survey Data File Dataset 1 contains data on school characteristics, HSP engagement indicators, baseline and follow-up responses to the Healthy Schools Inventory, and indices derived from the Inventory for all HSP schools as of August 2011. The Inventory collected information about each school's adherence to the Healthy Schools Program Framework, a set of best practice guidelines that promote physical activity and healthy eating among students and staff. Datasets 2, 4 and 6 contain data from baseline and follow-up administrations of the Healthy Schools Youth Survey questionnaire in three samples of HSP schools: students in grades 5-12 in the initial pilot cohort of HSP schools; students in grades 5, 8 and 10 in the 2007-2008 cohort of HSP schools in Boston, Massachusetts and Miami-Dade County, Florida; and students in grades 5, 8 and 10 or 11 in HSP schools in Arizona, Nevada and Prince George's County, Maryland. Topics covered by the Healthy Schools Youth Survey questionnaire include eating and physical activity habits, attitudes about healthy eating and physical activity, health knowledge, and school food environments. Datasets 3 and 7 contain baseline and follow-up height and weight measurements and derived BMIs, the former for students in grades 4-12 in schools sampled by the Pilot Student Survey and the latter for students in grades 5, 8, and 10 in Arizona and grades 1-12 in Prince George's County in schools sampled by the Arizona, Prince George's County and Nevada Healthy Schools Youth Survey. Dataset 5 is an update to Dataset 1. Like Dataset 1 it contains data on HSP participation and engagement and school characteristics. Dataset 5 covers 8,500 schools that participated in HSP through fall 2014. It includes 4,028 of the 4,542 schools in Dataset 1. Dataset 8 contains average math, reading and language scores for grades in HSP and comparable non-HSP schools in Arizona. Every record in the data file represents a grade (one or more of the grades 2-9) within a school (150 schools) for a given school year (up to seven years 2007-2008 to 2013-2014). Dataset 9 contains data from a survey of HSP scho

Data on the top universities for Computer Science in 2025.

In 2022, Darden School of Business in Charlottesville, Virginia was ranked as the 14th best business school in the United States, with an average student debt of 114,043 U.S. dollars and average starting salary and sign-on bonus of 186,974 U.S. dollars for MBA graduates. The number one ranked business school, the Wharton School of the University of Pennsylvania, had a average MBA grad starting salary of 186,279 U.S. dollars but did not report student debt figures for 2022. Only 11 out of the 25 top ranked business schools in the U.S. reported their average student debt in that year.

The Region 2 schools GIS layer contains unique records with basic identification information and best available locational information, for Region 2 schools found in the EPA Facility Registry System, which has imported records for schools in the National Center for Education Statistics database. Fields included in the layer include FRS_Name, FRS_Address, Facility Registry System ID), and locational information (latitude, longitude, and locational metadata). The locational data source for this and all R2 Regulated FACILITY and R2 Regulated PERMIT GIS Layers is the Locational Reference Tables (LRT) database, of Envirofacts augmented by R2 Locational Data Improvement records that may not yet have been cycled into the LRT. The Facility Registry System (FRS) is a centrally managed database developed by EPA's Office of Environmental Information (OEI). It provides Internet access to a single source of comprehensive information about facilities subject to environmental regulations or of environmental interest. The FRS contains accurate and authoritative facility identification records which are subjected to rigorous verification and data management quality assurance procedures. FRS records are continuously reviewed and enhanced by a Regional Data Steward network and active State partners. The facility records are based on information from EPA's national program systems and State master facility records and enhanced by other Web information sources.

Data on the top universities for Engineering in 2025, including disciplines such as Chemical Engineering, Civil Engineering, and Mechanical and Aerospace Engineering.

Data on the top universities for Law in 2025.

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

The number of students in regular programs for youth, general programs for adults, and vocational programs for youth and adults in public and private/independent schools, and home-schooling at the elementary-secondary level, by school type and program type.

In 2023, the top ranked full-time business school in the United States was the Stanford Graduate School of Business in Stanford, California, where tuition costs students a total of 80,613 U.S. dollars.

Introduction: In an age of increasingly face-to-face, blended, and online Health Professions Education, students have more selections of where they will receive a degree. For an applicant, oftentimes, the first step is to learn more about a program through its website. Websites allow programs to convey their unique voice and to share their mission and values with others, such as applicants, researchers, and academics. Additionally, as the number of Health Professions Education programs rapidly grows, websites can share the priorities of these programs. Methods: In this study, we conducted a website review of 158 Health Professions Education websites to explore their geographical distributions, missions, educational concentrations, and various programmatic components. Results: We compiled this information and synthesized pertinent aspects, such as program similarities and differences, or highlighted the omission of critical data. Conclusion: Given that websites are often the first point of contact for prospective applicants, curious collaborators, and potential faculty, the digital image of HPE programs matters. We believe our findings demonstrate opportunities for growth within institutions and assist the field in identifying the priorities of HPE programs. As programs begin to shape their websites with more intentionality, they can reflect their relative divergence/convergence compared to other programs as they see fit and, therefore, attract individuals to best match this identity. Periodic reviews of the breadth of programs, such as those undergone here, are necessary to capture diversifying goals, and serve to help advance the field of Health Professions Education as a whole. Methods Our team deduced that most HPE programs would have a website, and that this would serve as a representation of how individuals within the program choose to view themselves and hope to be viewed by others. Further, our team determined that these websites would be an efficient means of collecting programmatic information for the purposes of learning more about program growth, diversity, and values. We conducted the website review from August 2021 to April 2022 using a list of worldwide Health Professions Education programs, which was acquired from the Foundation of Advancement of International Medical Education and Research’s (FAIMER’s) website. FAIMER was chosen as the origin source of programs studied due to its use in another published study evaluating HPE programs. Each master's degree in HPE offered by a university was counted separately, allowing us to note the differences in course and time requirements across all programs. Only HPE master's programs were selected for this study. Certificate and Ph.D. programs were excluded. Next, we developed a data extraction tool. Categories were jointly identified for data collection by three of our authors (JS, SW, and HM). JS, SW, and HW worked independently through a set of three HPE programs, obtaining the data for our selected categories. Afterward, we cross-checked each other's work for verification purposes. For example, if JS obtained the information, SW or HM, who were blinded to JS’s findings, would independently find the answers to the same questions/ topics. This was performed until an agreement between pre and post-review information was above 95%. There was no discovered information that was not agreed upon after discussion. Once 100% agreement was reached with this method, the total number of HPE programs analyzed was split between JS and SW, and the raw data was obtained for the same categories. This data then underwent a review by the other two researchers to ensure high accuracy. This review consisted of information verification on individual program websites where it was originally obtained. For example, if JS found the information about a program, SW and HM (now not blinded) would both have to independently find the same information. Any identified discrepancies were rectified through discussion, and three-way agreement was mandatory for the team to move on to the next program.

About this dataset

Context

The dataset tabulates the population of Iuka by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Iuka across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.86% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Iuka is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Iuka total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Iuka Population by Race & Ethnicity. You can refer the same here

Abstract (en): These data were collected as part of the evaluation of the Healthy School Program (HSP), a program that provides support to elementary, middle, and high schools in the United States as they work to create healthy school environments that promote physical activity and healthy eating for students and staff. HSP was created in 2006 by the Alliance for a Healthier Generation with funding from the Robert Wood Johnson Foundation. The HSP evaluation addressed both process and impact outcomes:

Is the HSP technical assistance and training model effective in increasing the implementation of policies and programs that promote and provide access to healthier foods and more physical activity before, during and after school? ;

Are there distinctive or common school-level characteristics that hasten or hinder school-level implementation of policies and programs that promote and provide access to healthy foods and physical activity in the school setting in HSP schools? ;

Does participation in HSP contribute to an increase in healthy eating behaviors and physical activity participation among students? Does participation in HSP contribute to a decrease in body mass index (BMI) among students? ; The evaluation used a mixed-method design incorporating both quantitative and qualitative components. The quantitative component of the evaluation was a longitudinal design that measured student changes in eating and physical activity behaviors and BMI and schools' implementation of policies and practices promoted by HSP. For the qualitative component the evaluation team conducted site visits in a sample of HSP schools.

Nine data files constitute this data collection:

HSP Participation and Inventory Data File, 2006-2011 (originally called the Inventory Data File) ;

Pilot Student Survey Data File ;

Pilot Student Height and Weight Measurements Data File ;

Survey of Students in Boston and Miami-Dade Public Schools Data File ;

HSP Participation and Inventory Data File, 2006-2014 ;

Arizona, Prince George's County and Nevada Healthy Schools Youth Survey Data File ;

Arizona and Prince George's County Youth Height and Weight Measurements Data File ;

Arizona Academic Achievement Data File ;

Prince George's County School Wellness Coordinator Survey Data File ;

Dataset 1 contains data on school characteristics, HSP engagement indicators, baseline and follow-up responses to the Healthy Schools Inventory, and indices derived from the Inventory for all HSP schools as of August 2011. The Inventory collected information about each school's adherence to the Healthy Schools Program Framework, a set of best practice guidelines that promote physical activity and healthy eating among students and staff.

Datasets 2, 4 and 6 contain data from baseline and follow-up administrations of the Healthy Schools Youth Survey questionnaire in three samples of HSP schools: students in grades 5-12 in the initial pilot cohort of HSP schools; students in grades 5, 8 and 10 in the 2007-2008 cohort of HSP schools in Boston, Massachusetts and Miami-Dade County, Florida; and students in grades 5, 8 and 10 or 11 in HSP schools in Arizona, Nevada and Prince George's County, Maryland. Topics covered by the Healthy Schools Youth Survey questionnaire include eating and physical activity habits, attitudes about healthy eating and physical activity, health knowledge, and school food environments.

Datasets 3 and 7 contain baseline and follow-up height and weight measurements and derived BMIs, the former for students in grades 4-12 in schools sampled by the Pilot Student Survey and the latter for students in grades 5, 8, and 10 in Arizona and grades 1-12 in Prince George's County in schools sampled by the Arizona, Prince George's County and Nevada Healthy Schools Youth Survey.

Dataset 5 is an update to Dataset 1. Like Dataset 1 it contains data on HSP participation and engagement and school characteristics. Dataset 5 covers 8,500 schools that participated in HSP through fall 2014. It includes 4,028 of the 4,542 schools in Dataset 1.

Dataset 8 contains average math, reading and language scores for grades in HSP and comparable non-HSP schools in Arizona. Every record in the data file represents a grade (one or more of the grades 2-9) within a school (150 schools) for a given school year (up to seven years 2007-2008 to 2013-2014).

Dataset 9 contains data from a survey of HSP school coordinators in Prince Georges County. The coordinators wer...

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

The recent movement underscoring the importance of career taxonomies has helped usher in a new era of transparency in PhD career outcomes. The convergence of discipline-specific organizational movements, interdisciplinary collaborations, and federal initiatives has helped to increase PhD career outcomes tracking and reporting. Transparent and publicly available PhD career outcomes are being used by institutions to attract top applicants, as prospective graduate students are factoring in these outcomes when deciding on the program and institution in which to enroll for their PhD studies. Given the increasing trend to track PhD career outcomes, the number of institutional efforts and supporting offices for these studies have increased, as has the variety of methods being used to classify and report/visualize outcomes. This report comprehensively synthesizes existing PhD career taxonomy tools, resources, and visualization options to help catalyze and empower institutions to develop and publish their own PhD career outcomes. Similar fields between taxonomies were mapped to create a new crosswalk tool, thereby serving as an empirical review of the career outcome tracking systems available. Moreover, this work spotlights organizations, consortia, and funding agencies that are steering policy changes toward greater transparency in PhD career outcomes reporting. Such transparency not only attracts top talent to universities, but also propels research progress and technological innovation forward. Therefore, university administrators must be well-versed in government policies that may impact their PhD students. Engaging with government relations offices and establishing dialogues with policymakers are crucial steps toward staying informed about relevant legislation and advocating for more resources. For instance, much of the recent science legislation in the U.S. Congress, including the Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, significantly impacts federal agency programs influencing universities. To ensure sustained development, it is imperative to support initiatives that enhance transparency, both in terms of legislation and resources. Increased funding for programs supporting transparency will aid legislatures and institutions in staying informed and responsive. Many efforts presented in this publication have received support from federal and state governments or philantrophic sources, underscoring the need for multifaceted support to initiate and perpetuate this level of systemic change.

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.

The number of graduates by institution type, program type, credential type, gender and Classification of Instructional Programs, Primary groupings (CIP_PG).