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

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

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

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

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

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

School learning modality types are defined as follows:

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

Data Information

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

Technical Notes

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

Sources

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

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.

Dataset Description This dataset consists of academic and demographic information about 300 students from a university, which can be used for predicting academic outcomes, such as probation status. The dataset was simulated to represent a variety of student attributes across multiple categories like personal data, academic history, and other related information. The primary goal of this dataset is to analyze factors contributing to academic performance and identify students at risk of probation.

Column Descriptions Student No.: (Numeric) A unique identifier for each student. In this dataset, each student has a different ID number, making it a 100% unique column. Cohort: (Numeric) The year a student enrolled in the university. No missing values and consistent across the dataset. College: (Nominal) The name of the college the student belongs to. Examples include "Engineering," "Science," etc. No missing values. College Code: (Nominal) A numerical or alphanumerical code representing the college. This is an alternative representation of the "College" column. Major: (Nominal) The major field of study of the student. Some missing values (23%) represent students who haven’t declared a major or are in an undeclared status. Major Code: (Nominal) A code representing the major subject. Similar to the "Major" column, this has 23% missing values due to undeclared majors. Minor: (Nominal) The minor subject, if any, chosen by the student. This column has a high percentage of missing data (91%) since most students do not have minors. Spec: (Nominal) Specialization within the major field of study. Like the "Minor" column, this has 93% missing data as most students do not declare a specialization. Degree: (Numeric) The type of degree the student is pursuing (e.g., Bachelor's). In this dataset, all students are pursuing the same degree, so there are no missing values. Status: (Nominal) The current academic standing of the student (e.g., "Active," "Inactive"). No missing values. Load Status: (Nominal) The academic load status (e.g., "Full-time," "Part-time"). This column has very few missing values (1%). Gender: (Nominal) The gender of the student (e.g., "Male," "Female"). No missing values. Country: (Nominal) The country of origin of the student. Only 2 missing values, making it nearly complete. Governorate: (Nominal) The administrative region (governorate) the student comes from. This column has a small percentage of missing values (1%). Wellayah: (Nominal) The district or locality within the governorate. Around 1% of the data is missing. CGPA: (Numeric) The cumulative grade point average (CGPA) of the student. This field has 145 missing values, representing students without available CGPA records. Estimated Graduation Year: (Numeric) The expected year in which the student will graduate. No missing values. From HEAC: (Nominal) Indicates whether the student was admitted through the Higher Education Admission Center (HEAC). This column has 4% missing values. Admission Category: (Nominal) The category of admission (e.g., scholarship, self-funded). This column has a significant amount of missing data (98%), indicating that admission category data is either unavailable or irrelevant for most students. Birth Date: (Nominal) The birth date of the student. The dataset includes very few missing values (0%) and has been replaced by the derived feature "Age." Actual Graduation Date: (Nominal) The actual date on which a student graduates. More than half of the values are missing (54%), representing students who haven’t graduated yet. Withdrawal: (Nominal) Indicates whether the student has withdrawn from the university. This column has 89% missing data since the majority of students haven’t withdrawn. Marital Status: (Nominal) The marital status of the student (e.g., "Single," "Married"). No missing values. SQU Hostel: (Nominal) Indicates whether the student lives in the university hostel. No missing values. Percentage (Secondary School Score): (Nominal) The student’s percentage score from secondary school. No missing values. Probation Student: (Nominal) Indicates whether the student is under academic probation. This is the target variable for classification, with no missing values.

Record Details Total Records: 300 Total Attributes: 26 Missing Values: Some columns have a significant proportion of missing data (e.g., Minor, Spec, Major Code), while others have very few or no missing values (e.g., Gender, Cohort, College). Missing values were handled using a placeholder for clarity in certain columns.

Content

This dataset consists of a row for every accredited high school in New York City with its department ID number, school name, borough, building code, street address, latitude/longitude coordinates, phone number, start and end times, student enrollment with race breakdown, and average scores on each SAT test section for the 2014-2015 school year.

Acknowledgements

The high school data was compiled and published by the New York City Department of Education, and the SAT score averages and testing rates were provided by the College Board.

Inspiration

Which public high school's students received the highest overall SAT score? Highest score for each section? Which borough has the highest performing schools? Do schools with lower student enrollment perform better?

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week. Data from August

Postsecondary enrolments, by detailed field of study, institution, institution type, registration status, program type, credential type, status of student in Canada and gender.

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their place of study, for the census usually resident population count who are studying (part time or full time), by main means of travel to education from the 2018 and 2023 Censuses.

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The main means of travel to education categories are:

• Study at home
• Drive a car, truck, or van
• Passenger in a car, truck, or van
• Bicycle
• Walk or jog
• School bus
• Public bus
• Train
• Ferry
• Other.

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Main means of travel to education is the usual method a person used to travel the longest distance to their place of study.

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Educational institution address is the physical location of the individual’s place of study. Educational institutions include early childhood education, primary school, secondary school, and tertiary education institutions. For individuals who study at home, their educational institution address is the same as their usual residence address.

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Educational institution address is coded to the most detailed geography possible from the available information. This dataset only includes travel to education information for individuals whose educational institution address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the census usually resident population count who are studying (part time or full time) for that region. Educational institution address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Educational institution address time series

Educational institution address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Educational institution address – 2023 Census: Information by concept has more information.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of educational institution address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to education quality rating

Main means of travel to education is rated as moderate quality.

Main means of travel to education – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Educational institution address quality rating

Educational institution address is rated as moderate quality.

Educational institution address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

About this dataset

Context

The dataset presents median household incomes for various household sizes in College Place, WA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, College Place did not include 6-person households. Across the different household sizes in College Place the mean income is $103,729, and the standard deviation is $60,811. The coefficient of variation (CV) is 58.62%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $33,704. It then further increased to $200,107 for 7-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in College Corner, OH, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, College Corner did not include 4, 5, 6, or 7-person households. Across the different household sizes in College Corner the mean income is $61,928, and the standard deviation is $37,242. The coefficient of variation (CV) is 60.14%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $23,308. It then further increased to $64,855 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/college-corner-oh-median-household-income-by-household-size.jpeg" alt="College Corner, OH median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in University Park, IA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, University Park did not include 5, or 7-person households. Across the different household sizes in University Park the mean income is $76,260, and the standard deviation is $36,768. The coefficient of variation (CV) is 48.21%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $19,930. It then further increased to $95,737 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/university-park-ia-median-household-income-by-household-size.jpeg" alt="University Park, IA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 University Park median household income. You can refer the same here