Turkey was the European country which had the largest share of its citizens self-reporting that they could not speak any foreign language, with almost 82 percent saying they were unable to do so. The United Kingdom and Bosnia and Herzegovina also had over 60 percent of their citizens self-reporting not being able to speak a foreign language. On the other hand, Slovenia, Sweden, and Estonia all had less than five percent of their populations stating they could not speak another language other than their country's main language. Slovenia, Luxembourg, and Norway were the three countries with the most citizens stating they could speak three foreign languages. On average, 37 percent of EU citizens report speaking one foreign language, 22 percent speak two, and 8.6 percent speak three, while 32 percent report speaking none.

Ireland stands as an outlier, as all citizens of the north-western European country self-reported as speaking a foreign language. This is, however, actually, a result of how the question was asked, as respondents interpreted English as being a foreign language (in the sense of not being native to Ireland), in spite of it being one of the two official languages in the country (alongside Irish) and being spoken by the vast majority of the population in Ireland as their first language.

In 2024, some 45 million people in the United States spoke Spanish at home. In comparison, the second most spoken non-English language spoken by households was Chinese, at just 3.7 million speakers.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.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

**🌍 World Countries Dataset This World Countries Dataset contains detailed information about countries across the globe, offering insights into their geographic, demographic, and economic characteristics.

It includes various features such as population, area, GDP, languages, and regional classifications. This dataset is ideal for projects related to data visualization, statistical analysis, geographical studies, or machine learning applications such as clustering or classification of countries.

This dataset was manually compiled/collected from reliable open data sources (e.g., Wikipedia, World Bank, or other governmental datasets).

**🔍 Sample Questions Explored Using Python: - Q. 1) Which countries have the highest and lowest population? - Q. 2) What is the average area (in sq. km) of countries in each region? - Q. 3) Which countries have more than 100 million population and GDP above $1 trillion? - Q. 4) Which languages are most commonly spoken across countries? - Q. 5) Show a bar graph comparing GDPs of G7 nations. - Q. 6) How many countries are there in each continent or region? - Q. 7) Which countries have both a high population density and low GDP per capita? - Q. 8) Create a world map visualization of population or GDP distribution. - Q. 9) What are the top 10 most densely populated countries? - Q. 10) How many landlocked countries are there in the world?

**🧾 Features / Columns in the Dataset: - Country: The name of the country (e.g., "Pakistan", "France").

• Capital: The capital city of the country.

• Region: Broad geographical region (e.g., "Asia", "Europe").

• Subregion: More specific geographical grouping (e.g., "Southern Asia").

• Population: Total population of the country.

• Area (sq. km): Total land area in square kilometers.

• Population Density: Number of people per square kilometer.

• GDP (USD): Gross Domestic Product (in U.S. dollars).

• GDP per Capita: GDP divided by the population.

• Official Languages: Officially recognized language(s) spoken.

• Currency: Name of the currency used.

• Timezones: Timezones in which the country falls.

• Borders: List of bordering countries (if any).

• Landlocked: Whether the country is landlocked (Yes/No).

• Latitude / Longitude: Coordinates for geographical plotting.

Dataset Description: The AI-Enhanced English and Chinese Language Learning Dataset is a comprehensive collection of data aimed at advancing language education through the use of artificial intelligence. This dataset includes detailed records from various language learning platforms, combining both traditional classroom activities and AI-driven learning applications. The dataset is suitable for exploring different AI techniques to improve English and Chinese language acquisition, focusing on adaptive learning, feedback analysis, and language practice. Data spans from February 2019 to August 2024, covering diverse language learning scenarios across multiple institutions, including digital language labs, mobile apps, and AI-powered tutoring systems.

The dataset includes hourly data collected from language learners engaging in various activities such as grammar exercises, conversational practice, writing assessments, and interactive quizzes. The data is sourced from multiple regions, including English-speaking and Mandarin-speaking communities, making it ideal for comparative studies on AI-driven learning outcomes. The records encompass a variety of linguistic features and learning metrics, offering valuable insights into student engagement, progress, and performance across different learning contexts.

Features: Timestamp: Hourly timestamp indicating the time of each learning session. Learner ID: A unique identifier for each learner. Age: The age of the learner. Gender: Gender of the learner (Male, Female, Other). Native Language: The primary language spoken by the learner. Country of Residence: The country where the learner is based. Language Proficiency Level (Initial): The learner's initial language proficiency in English or Chinese (Beginner, Intermediate, Advanced). Type of Activity: Type of learning activity (Listening, Speaking, Reading, Writing). Lesson Content Type: The specific focus of the lesson (Grammar, Vocabulary, Pronunciation, etc.). Number of Lessons Completed: Cumulative count of lessons completed by the learner. Time Spent on Learning: Total time spent on language learning (in minutes). Learning Platform or Tool Used: Platform or tool used for learning (App, Website, Classroom Software). Homework Completion Rate: Percentage of homework assignments completed. Participation in Interactive Exercises: Frequency of participation in interactive exercises like quizzes and games. Frequency of Practice Sessions: Number of practice sessions per week. Test Scores: Scores from language proficiency tests, covering various areas such as grammar, listening, and vocabulary. Speaking Fluency Scores: Scores evaluating pronunciation accuracy and speech rate. Reading Comprehension Scores: Assessment scores for reading comprehension tasks. Writing Quality: Evaluation of writing quality based on grammatical accuracy and vocabulary use. Change in Proficiency Level: Measured change in language proficiency over time. Assignment Grades: Grades received on language assignments. Error Correction Rate: The rate at which learners correct their mistakes. Feedback from Instructors/Tutors: Qualitative feedback provided by instructors or AI tutors. Study Session Duration: Average duration of study sessions. Learning Consistency: Number of days per week studied. User Activity Type: Type of user activity (Active or Passive Participation). Engagement with Additional Learning Materials: Frequency of accessing extra learning resources (e.g., videos, articles). Peer Interaction Score: Score representing participation in study groups or discussion forums. Motivation Level: Self-reported level of motivation. Learning Environment: Type of learning environment (Home, School, Language Center). Learning Mode: Mode of learning (Self-Paced or Instructor-Led). Accessibility of Learning Resources: Availability of learning materials to the learner. Use of AI Tools: Whether AI tools like chatbots or speech recognition software were used. Language Learning Goals: Purpose of language learning (Academic, Professional, Personal). This dataset offers rich data for researchers and educators to analyze the impact of AI on language learning outcomes, make cross-linguistic comparisons, and develop personalized AI-driven language education models.

The Catalan and Spanish languages coexist in the coastal region of Catalonia, both enjoying official and equal status. As of 2024, more than ** percent of the population of Catalonia considered Spanish their mother tongue, whereas less than ** percent reported being native speakers of Catalan. Catalonia was the second most populous autonomous community in Spain in 2024 with about * million people. Editorial scene in Catalonia Despite the fact that the vast majority of books in Spain are published in Spanish, the Catalan language ranked second in the country’s editorial scene at about * percent of book publications, revealing the weight of this language among other languages spoken in Spain. In fact, Catalan was one of the most translated languages in this country according to the latest studies. Catalonia in Spain The Catalan participation in the Spanish GDP was estimated at ** percent in 2023. This figure maintained steadily over the last few years, with an average share of about ** percent of the total GDP of the country. The average GDP per capita in Catalonia was significantly higher than that of the rest of Spain at ****** euros in 2022. During the same period, Spain’s average GDP per capita was ****** euros.

Most residents of Catalonia considered themselves equally Catalan and Spanish. This is the result of a survey conducted in March and May 2025 which revealed that approximately 37 percent of the population in this northern region identified Catalan and Spanish to the same degree. The share of the population that identified as more Catalan than Spanish or only Catalan, however, was also significant, with shares of 20 percent and 15 percent, respectively. Catalonia in SpainThe Catalan participation in the Spanish GDP was estimated at 20 percent in 2024. This figure maintained steadily over the last few years, with an average share of about 20 percent of the total GDP of the country. The average GDP per capita in Catalonia was significantly higher than that of the rest of Spain at 34,534 euros in 2022. During the same period, Spain’s average GDP per capita was 28.276 euros. As of 2024, Catalonia was the second most populous autonomous community in Spain with about eight million people. The Catalan language: a symbol of the region’s identityThe Catalan and Spanish languages coexist in the coastal region of Catalonia, both enjoying official and equal status. As of 2024, about 47 percent of the population of Catalonia considered Spanish their mother tongue, whereas about 40 percent reported being native speakers of Catalan. Despite the fact that the vast majority of books in Spain are published in Spanish, the Catalan language ranked second in the country’s editorial scene at about nine percent of book publications, revealing the weight of this language among other languages spoken in Spain. In fact, Catalan was one of the most translated languages in this country according to the latest studies.