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The QS Rankings, renowned for its esteemed university evaluations, annually releases the QS World University Rankings. The 2024 edition comprises a dataset encompassing the top 100 universities globally, with each entry defined by 12 features.

The 'rank' feature denotes the university's position in the QS rankings, offering a quantitative representation of its standing. The 'university' column identifies the institution by name. The 'overall score' is a floating-point value derived from various contributing factors, reflecting the comprehensive evaluation undertaken by QS.

Academic reputation, an integral aspect, is quantified in the 'academic reputation' feature, while 'employer reputation' gauges the institution's standing in the professional realm. The 'faculty student ratio' is calculated by dividing the faculty count by the number of students, a metric often indicative of the learning environment's quality.

'Citations per faculty' delves into the scholarly impact, measuring the total citations received by an institution's papers over five years, normalized by faculty size. The 'international faculty ratio' and 'international students ratio' shed light on the global diversity of the academic community, capturing the proportion of foreign faculty and students.

The 'international research network' employs a formula to quantify the institution's global partnerships and collaborations. 'Employment outcomes' are assessed through a formula involving alumni impact and graduate employment indices, providing insights into the professional success of graduates.

Finally, the 'sustainability' feature evaluates an institution's commitment to environmental sciences, considering alumni outcomes and academic reputation within the field. It also examines the inclusion of climate science and sustainability in the curriculum, reflecting the growing emphasis on environmental consciousness in higher education.

In essence, this dataset encapsulates a multifaceted evaluation of universities worldwide, encompassing academic, professional, and sustainability dimensions, making it a valuable resource for individuals and institutions navigating the dynamic landscape of global higher education. VALUE FOUNDS IS HIPOTICALY data 2021

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.

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.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

Comprehensive dataset of college football teams ranked by all time wins. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Comprehensive dataset of college football teams ranked by all time record. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

About this dataset

Context

The dataset tabulates the data for the College Place, WA population pyramid, which represents the College Place population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for College Place, WA, is 28.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for College Place, WA, is 30.2.
• Total dependency ratio for College Place, WA is 58.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for College Place, WA is 3.3.

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

Variables / Data Columns

• Age Group: This column displays the age group for the College Place population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the College Place for the selected age group is shown in the following column.
• Population (Female): The female population in the College Place for the selected age group is shown in the following column.
• Total Population: The total population of the College Place for the selected age group is shown in the following column.

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 College Place Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the College Park, MD population pyramid, which represents the College Park population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for College Park, MD, is 10.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for College Park, MD, is 7.5.
• Total dependency ratio for College Park, MD is 17.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for College Park, MD is 13.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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

Variables / Data Columns

• Age Group: This column displays the age group for the College Park population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the College Park for the selected age group is shown in the following column.
• Population (Female): The female population in the College Park for the selected age group is shown in the following column.
• Total Population: The total population of the College Park for the selected age group is shown in the following column.

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 College Park Population by Age. You can refer the same here

Comprehensive dataset of college football teams ranked by bowl games. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

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.

Comprehensive dataset of college football teams ranked by first round nfl draft picks. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Abstract copyright UK Data Service and data collection copyright owner.The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables. Due to the closure of school sites during the coronavirus pandemic, the Active Lives Children and Young People survey was adapted to allow at-home completion. This approach was retained into the academic year 2022-23 to help maximise response numbers. The at-home completion approach was actively offered for secondary school pupils, and allowed but not encouraged for primary pupils. The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and enabling completion at home) and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic years 2020-21, 2021-22 and 2022-23 saw a more even split of responses by term across the year, compared to 2019-20, which had a reduced proportion of summer term responses due to the disruption caused by Covid-19. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year. The following datasets have been provided: 1) Main dataset: this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels; they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them). 2) Year 1-2 dataset: This file includes responses directly from children in school years 1-2, providing their attitudinal responses (e.g., whether they like playing sport and find it easy). Analysis can also be carried out into feelings towards swimming, enjoyment of being active, happiness, etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them). 3) Teacher dataset: This file includes responses from the teachers at schools selected for the survey. Analysis can be carried out to determine school facilities available, the length of PE lessons, whether swimming lessons are offered, etc. Since December 2023, Sport England has provided weighting for the teacher data (‘wt_teacher’ weighting variable). For further information, please read the supporting documentation before using the datasets.

The Dataset has information of ICPC world final's top team of last 10 years. Total Year - 10 Total year consists - Top 50 team Total Team = 500

What is ICPC? The International Collegiate Programming Contest is an algorithmic programming contest for college students. Teams of three, representing their university, work to solve the most real-world problems, fostering collaboration, creativity, innovation, and the ability to perform under pressure. Through training and competition, teams challenge each other to raise the bar on the possible. Quite simply, it is the oldest, largest, and most prestigious programming contest in the world. [Source - https://icpc.global/ ]

What can we do using this Dataset? ~We will able to predict the next champion and top most team ~By analysis , we can predict success rate , rate of problem solving etc.

IntroductionWorldwide burnout prevalence among medical students is high. It has a negative impact on students’ personal and professional lives as well as on their psychosocial wellbeing and academic performance. It can result in physicians with emotional distancing and indifference to work, and it compromises the quality of healthcare offered to society. This study evaluates burnout in medical students selected by mini-multiple interviews (MMIs) who were being taught by the team-based learning (TBL) method. MMIs are often used to select students with soft skills for medicine, and TBL is related to greater academic achievement, which would allow students to have greater resilience to stress. Information on burnout occurrence is lacking for this type of student.MethodsStudents (N = 143) attending the first three semesters at a private medical school were evaluated. The Copenhagen Burnout Inventory—Student Version (CBI-SV) questionnaire was applied on three occasions (applications = Apps one, two, and three) in each semester. Scores ≥ 50 were considered to indicate burnout. Data were analyzed by statistics programs.ResultsPersonal-related and study-related burnout frequencies for 1st semester students were, respectively, 24.4 and 22% in App one and rose to 51 and 48.5% at the semester’s end. Second- and third-semester students’ frequencies reached 80.4 and 78.8%, respectively. Around 40% of 1st semester students having burnout at App one maintained the burnout score. Peer- and teacher-related burnout frequencies are low (4.9 and 2.4%) at the 1st semester App one and rose to the highest (24–30%) by the end of the 2nd semester. Woman students had significantly higher burnout frequencies in the personal- (p < 0.001) and study-related burnout subscales (p = 0.003). Students living with friends had lower study-related burnout scores than those living with family or alone (p = 0.024). There were no significant correlations between the burnout scores and tuition funding (partial or total) or having or not having religious faith.DiscussionThe prevalence of personal- and study-related burnout among medical students of the Faculdade Israelita de Ciências da Saúde Albert Einstein (FICSAE), perceived via mini-multiple interviews (MMI)—selected and team-based learning (TBL)—taught, was similar to those internationally reported. The college semester and the gender of woman were associated with worse burnout levels. Additional studies are needed to support more effective actions to reduce the impact of stress on students.

ObjectiveResearch to date has not provided a clear understanding of how different grades and majors affect the physical fitness of college students. It is postulated that there are significant disparities in physical health among college students of different grades and majors. The purpose of this study was to evidence these health disparities and to engage in an extensive analysis and discussion thereof.MethodsA sample of 8,772 (2,404 boys and 6,368 girls) Chinese college students from freshman to junior years, aged 17–22, including 12 different majors in four colleges, were recruited in Jiangxi Province. All seven physical fitness indicators (body mass index (BMI), forced vital capacity, 50-m dash, standing long jump, sit and reach, upper body muscle strength, and endurance runs) were conducted for all participants. One-way ANOVA and LSD tests were conducted to compare the physical fitness scores of different grades in the same major. Independent sample t-tests were utilized to compare the differences in every physical fitness indicator for different majors. Pearson’s correlations among 12 majors for every grade were conducted to study the significant corrections between the two physical fitness indicators. The body mass index (BMI) and physical fitness indicator (PFI) for college students of different grade were investigated using a nonlinear regression model.ResultsThe current state of physical fitness among college students is concerning, as the majority of students were barely passing (with a passing rate of 75.3%). Specifically, junior students exhibited lower scores than freshman and sophomore students across all 12 majors. From freshman to junior year, majors of music (78.01±4.58), English (79.29±5.03), and education (76.26±4.81) had the highest scores, respectively, but major art consistently scored the lowest, which were 73.85±6.02, 74.97±5.53, and 72.59±4.84, respectively. Pairwise comparisons revealed more significant differences in individual physical fitness indicators among the three grades in humanities than in sciences. Pearson’s correlations showed significant correlations among seven physical fitness indicators in all three grades. PFI had a parabolic trend with BMI both for boy and girl college students in Jiangxi province.ConclusionThe physical fitness indicators of college students in Jiangxi province significantly differed in grades and majors, showing undesirable phenomena. The physical fitness of senior and humanities major college students was much weaker and needs sufficient attention. The relationship between BMI and PFI presented an inverted “U”-shaped change characteristic. Continued nationwide interventions are needed to promote physical activity and other healthy lifestyle behaviors in China.

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

A comprehensive dataset of top colleges for Green Card sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest data on Green Card sponsorship trends and employer behavior.

Statistics on postsecondary graduates, including the number of graduates, the percentage of female graduates and age at graduation, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

About this dataset

Context

The dataset tabulates the data for the College Corner, OH population pyramid, which represents the College Corner population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for College Corner, OH, is 27.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for College Corner, OH, is 28.0.
• Total dependency ratio for College Corner, OH is 55.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for College Corner, OH is 3.6.

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

Variables / Data Columns

• Age Group: This column displays the age group for the College Corner population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the College Corner for the selected age group is shown in the following column.
• Population (Female): The female population in the College Corner for the selected age group is shown in the following column.
• Total Population: The total population of the College Corner for the selected age group is shown in the following column.

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 College Corner Population by Age. You can refer the same here