In 2023, there were around 1.5 billion people worldwide who spoke English either natively or as a second language, slightly more than the 1.1 billion Mandarin Chinese speakers at the time of survey. Hindi and Spanish accounted for the third and fourth most widespread languages that year.

Languages in the United States The United States does not have an official language, but the country uses English, specifically American English, for legislation, regulation and other official pronouncements. The United States is a land of immigrations and the languages spoken in the United States vary as a result of the multi-cultural population. The second most common language spoken in the United States is Spanish or Spanish Creole, which over 41 million people spoke at home in 2021. There were also 3.5 million Chinese speakers (including both Mandarin and Cantonese),1.7 million Tagalog speakers and 1.5 million Vietnamese speakers counted in the United States that year.

Different languages at home The percentage of people in the United States speaking a language other than English at home varies from state to state. The state with the highest percentage of population speaking a language other than English is California. About 44 percent of California’s population was speaking a language other than English at home in 2021.

As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

In 2022, around 42.03 million people in the United States spoke Spanish at home. In comparison, approximately 974,829 people were speaking Russian at home during the same year. The distribution of the U.S. population by ethnicity can be accessed here. A ranking of the most spoken languages across the world can be accessed here.

This dataset shows the most spoken languages by borough and MSOAs in London. It provides numbers of the population aged 3+ who speak specified languages as their main language.

Main language is from 2011 Census (detailed) - Census table QS204EW.

This data is presented alongside Annual Population Survey (APS) data showing the top nationalities of residents in January - December 2019 by borough. The top 3 non-British nationalities are at the far right of the table. This is to highlight areas which may now have other common non-British languages spoken compared to 2011 (the year in which the Census information was gathered). The top non-British nationalities in 2019, which did not feature in 2011 as one of the most spoken non-British languages, are highlighted in column AD.

The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. Estimates for non-British nationalities at borough level that are below 10,000 are considered too small to be reliable and should be treated with additional caution.

MSOA codes have now been linked to House of Commons MSOA names

The statistic reflects the distribution of languages in Canada in 2022. In 2022, 87.1 percent of the total population in Canada spoke English as their native tongue.

Language spoken at home and the ability to speak English for the population age 5 and over as reported by the US Census Bureau's, American Community Survey (ACS) 5-year estimates table C16001.

Mexico is the country with the largest number of native Spanish speakers in the world. As of 2024, 132.5 million people in Mexico spoke Spanish with a native command of the language. Colombia was the nation with the second-highest number of native Spanish speakers, at around 52.7 million. Spain came in third, with 48 million, and Argentina fourth, with 46 million. Spanish, a world language As of 2023, Spanish ranked as the fourth most spoken language in the world, only behind English, Chinese, and Hindi, with over half a billion speakers. Spanish is the official language of over 20 countries, the majority on the American continent, nonetheless, it's also one of the official languages of Equatorial Guinea in Africa. Other countries have a strong influence, like the United States, Morocco, or Brazil, countries included in the list of non-Hispanic countries with the highest number of Spanish speakers. The second most spoken language in the U.S. In the most recent data, Spanish ranked as the language, other than English, with the highest number of speakers, with 12 times more speakers as the second place. Which comes to no surprise following the long history of migrations from Latin American countries to the Northern country. Moreover, only during the fiscal year 2022. 5 out of the top 10 countries of origin of naturalized people in the U.S. came from Spanish-speaking countries.

In 2020, about 93.8 percent of the Mexican population was monolingual in Spanish. Around five percent spoke a combination of Spanish and indigenous languages. Spanish is the third-most spoken native language worldwide, after Mandarin Chinese and Hindi.

Mexican Spanish

Spanish was first being used in Mexico in the 16th century, at the time of Spanish colonization during the Conquest campaigns of what is now Mexico and the Caribbean. As of 2018, Mexico is the country with the largest number of native Spanish speakers worldwide. Mexican Spanish is influenced by English and Nahuatl, and has about 120 million users. The Mexican government uses Spanish in the majority of its proceedings, however it recognizes 68 national languages, 63 of which are indigenous.

Indigenous languages spoken

Of the indigenous languages spoken, two of the most widely used are Nahuatl and Maya. Due to a history of marginalization of indigenous groups, most indigenous languages are endangered, and many linguists warn they might cease to be used after a span of just a few decades. In recent years, legislative attempts such as the San Andréas Accords have been made to protect indigenous groups, who make up about 25 million of Mexico’s 125 million total inhabitants, though the efficacy of such measures is yet to be seen.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

The primary languages spoken at home in Nigeria are Hausa, Yoruba, and English. In a survey conducted in 2022 around 32 percent of respondents declared that they mainly spoke Hausa at home. Some 17 percent, on the other hand, had Yoruba as their main family language. Igbo followed, with 13 percent of the respondents indicating it. Some other languages spoken in Nigerian households are English, Ibibio, Fulani, Tiv, Nupe, Pidgin English, and Ijaw.

One of the most diverse countries

There are over 500 languages in Nigeria. The country has only one official language, English. According to estimates from 2018, Nigeria's major ethnic groups are Hausa, Yoruba, Igbo (Ibo), Fulani, Tiv, Kanuri, and Beriberi. Hausa, the largest population, is an ethnic group of people speaking the Hausa language. The Hausa are mainly present in West Africa, most of them living between Nigeria and Niger.

English is the main language at school

The main language of instruction at school is generally English. However, for the first years of education, an indigenous or local language is also taught. As of 2019, around 72 percent of young women and 78 percent of young men in Nigeria were English language literates. This means they could understand, read, and write a short and simple statement in English, for instance, on their everyday life.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Japan Centre of Excellence (JACEEX), is a brand under Jaceex Ventures LLP. Jaceex has been formed with a vision to create a world class workforce with skill sets, work and business ethics, sincerity and devotion as well as other great positive traits found in the Japanese workforce which has been responsible for having built world class Enterprises. For the Indian Students and youths stepping into this world, our objective is to provide life changing opportunity in the form of skill and work in Japan Japan Centre of Excellence (JACEEX) provides an integrated course schedule of learning through exploration, scrutiny and self reflection. We are offering Japanese Language and Culture training-Basic, Intermediate and High Levels. Our training is designed to make the trainee eligible to certify themselves with the globally recognised Japanese Language Proficiency Test (JLPT) Examination . This will help in building careers with Japanese companies in Japan , in India and also self employment.We also have the facility of Virtual Live class platform

This dataset is adapted from raw data with fully anonymized results on the State Examination of Dutch as a Second Language. This exam is officially administred by the Board of Tests and Examinations (College voor Toetsen en Examens, or CvTE). See cvte.nl/about-cvte. The Board of Tests and Examinations is mandated by the Dutch government.

The article accompanying the dataset:

Schepens, Job, Roeland van Hout, and T. Florian Jaeger. “Big Data Suggest Strong Constraints of Linguistic Similarity on Adult Language Learning.” Cognition 194 (January 1, 2020): 104056. https://doi.org/10.1016/j.cognition.2019.104056.

Every row in the dataset represents the first official testing score of a unique learner.
The columns contain the following information as based on questionnaires filled in at the time of the exam:

"L1" - The first language of the learner
"C" - The country of birth
"L1L2" - The combination of first and best additional language besides Dutch
"L2" - The best additional language besides Dutch
"AaA" - Age at Arrival in the Netherlands in years (starting date of residence)
"LoR" - Length of residence in the Netherlands in years
"Edu.day" - Duration of daily education (1 low, 2 middle, 3 high, 4 very high). From 1992 until 2006, learners' education has been measured by means of a side-by-side matrix question in a learner's questionnaire. Learners were asked to mark which type of education they have had (elementary, secondary, or tertiary schooling) by means of filling in for how many years they have been enrolled, in which country, and whether or not they have graduated. Based on this information we were able to estimate how many years learners have had education on a daily basis from six years of age onwards. Since 2006, the question about learners' education has been altered and it is asked directly how many years learners have had formal education on a daily basis from six years of age onwards. Possible answering categories are: 1) 0 thru 5 years; 2) 6 thru 10 years; 3) 11 thru 15 years; 4) 16 years or more. The answers have been merged into the categorical answer.
"Sex" - Gender
"Family" - Language Family
"ISO639.3" - Language ID code according to Ethnologue
"Enroll" - Proportion of school-aged youth enrolled in secondary education according to the World Bank. The World Bank reports on education data in a wide number of countries around the world on a regular basis. We took the gross enrollment rate in secondary schooling per country in the year the learner has arrived in the Netherlands as an indicator for a country's educational accessibility at the time learners have left their country of origin.
"STEX_speaking_score" - The STEX test score for speaking proficiency.
"Dissimilarity_morphological" - Morphological similarity
"Dissimilarity_lexical" - Lexical similarity
"Dissimilarity_phonological_new_features" - Phonological similarity (in terms of new features)
"Dissimilarity_phonological_new_categories" - Phonological similarity (in terms of new sounds)

A few rows of the data:

"L1","C","L1L2","L2","AaA","LoR","Edu.day","Sex","Family","ISO639.3","Enroll","STEX_speaking_score","Dissimilarity_morphological","Dissimilarity_lexical","Dissimilarity_phonological_new_features","Dissimilarity_phonological_new_categories"
"English","UnitedStates","EnglishMonolingual","Monolingual",34,0,4,"Female","Indo-European","eng ",94,541,0.0094,0.083191,11,19
"English","UnitedStates","EnglishGerman","German",25,16,3,"Female","Indo-European","eng ",94,603,0.0094,0.083191,11,19
"English","UnitedStates","EnglishFrench","French",32,3,4,"Male","Indo-European","eng ",94,562,0.0094,0.083191,11,19
"English","UnitedStates","EnglishSpanish","Spanish",27,8,4,"Male","Indo-European","eng ",94,537,0.0094,0.083191,11,19
"English","UnitedStates","EnglishMonolingual","Monolingual",47,5,3,"Male","Indo-European","eng ",94,505,0.0094,0.083191,11,19

Combining human expertise with information from book-consumer digital data may generate what it takes to face the following changes in such a critical market. Along with the publishing industry, researchers rely on book-related data to develop tools and applications, drawing constructive conclusions to make better informed and faster decisions. Such solutions range from best-selling prediction models to natural language processing to classify raw text. Besides require complex Artificial Intelligence (AI) methods, all of them are essentially data-dependent, mainly book-related data-dependent.

Data, and more specifically data growth, is essential for developing and performing such AI-powered applications. None of these efforts can be achieved without a preliminary collection of data on literary works, readers, and their reading habits. Therefore, it is critically important to build and make available datasets that fully comprise the essential elements of the book industry ecosystem. Although some efforts have been made for English language books, little has been done regarding other lesser-spoken languages, such as Portuguese. The evaluation of specific data is of fundamental importance for literature analysis, as Portuguese has its own literary peculiarities. Hence, we present PPORTAL, a Public domain PORTuguese-lAnguage Literature dataset. PPORTAL's contributions are summarized as follows:

Data integration of numerous public domain works from three digital libraries;

Enriched metadata for works, authors and online reviews extracted from Goodreads;

Feature engineering on the metadata to create meaningful additional features; and

Unrestricted access in two formats (SQL database and compressed .csv files

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

As of 2021, about 43.9 percent of California's population was speaking a language other than English at home. A ranking of the most spoken languages across the world can be accessed here.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.