Examining international student demographics helps educational institutions better understand the diverse backgrounds and requirements of their global student community. This dataset provides insights into a variety of aspects including, gender, marital status, Visa type, origin of country, academic level, and much more.

For use case and analysis reference, please take a look at this notebook "https://www.kaggle.com/code/webdevbadger/international-student-demographics-analysis">International Student Demographics Analysis .

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16711385%2Fefde694297c2830c0058032eae820358%2Ftop-countries.png?generation=1704958145733418&alt=media" alt="">

Feature Descriptions

academic.csv

• year: The year. The format is YYYY/YY.
• students: The number of students.
• us_students: The number of non-international students.
• undergraduate: The number of undergraduate students.
• graduate: The number of graduate students.
• non_degree: The number of non-degree students.
• opt: The number of OPT students. OPT stands for Optional Practical Training.

academic_detail.csv

• year: The year. The format is YYYY/YY.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"],
• academic_level: The academic level. One of ["Associate's", "Bachelor's", "Master's", 'Doctoral', "Professional", "Graduate, Unspecified", "Non-Degree, Intensive English", "Non-Degree, Other", "OPT"].
• students: The number of students.

field_of_study.csv

• year: The year. The format is YYYY/YY.
• field_of_study: The field of the study.
• major: The major of the study.
• students: The number of students.

origin.csv

• year: The year. The format is YYYY/YY.
• origin_region: The region of origin, such as Asia, Europe, and North America.
• origin: The origin, such as Canada, China, and India.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"].
• students: The number of students.

source_of_fund.csv

• year: The year. The format is YYYY/YY.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"].
• source_type: The fund source type. One of ["International", "U.S.", "Other"].
• source_of_fund: The source of fund. One of [ "Personal and Family", "Foreign Government or University", "Foreign Private Sponsor", "International Organization", "Current Employment", "U.S. College or University", "U.S. Government", "U.S. Private Sponsor", "Other Sources"].
• students: The number of students.

status.csv

• year: The year. The format is YYYY/YY.
• female: The number of female students.
• male: The number of male students.
• single: The number of non-married students.
• married: The number of married students.
• full_time: The number of full-time students.
• part_time: The number of part-time students.
• visa_f: The number of students with F Visa.
• visa_j: The number of students with J Visa.
• visa_other: The number of students with other types of Visas.

Acknowledgement

OpenDoorsData.org

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The dataset contains All India Yearly Forex Reserves from Handbook of Statistics on Indian Economy.

Note: 1. Foreign Currency Assets exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI and foreign currency received under SAARC SWAP arrangement. Foreign currency assets in US dollar take into account appreciation/ depreciation of non-US currencies (such as Euro, Sterling, Yen, Australian Dollar, etc.) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates. 2. Gold Includes Rupees 31463 crore(US $ 6699 million) reflecting the purchase of 200 metric tonnes of gold from IMF on November 3, 2009. 3. SDRs Includes SDRs 3082.5 million allocated under general allocation and SDRs 214.6 million allocated under special allocation by the IMF done on August 28, 2009 and september 9, 2009, respectively.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

India's total Exports in 2024 were valued at US$434.44 Billion, according to the United Nations COMTRADE database on international trade. India's main export partners were: the United States, the United Arab Emirates and the Netherlands. The top three export commodities were: Mineral fuels, oils, distillation products; Electrical, electronic equipment and Machinery, nuclear reactors, boilers. Total Imports were valued at US$697.75 Billion. In 2024, India had a trade deficit of US$263.31 Billion.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

Context

This dataset deals with the visitors of foreigners to INDIA.

It includes foreigners (not Indian), overseas Indian, and crew members, except for some of the foreign arrivals who are not considered tourists (diplomats, soldiers, permanent residents, visiting cohabitation, and residence).

The Indian Government has compiled, analyzed, and provided statistics on foreign tourists visiting Indian and overseas tourists by type.

The data materials were prepared for the purpose of utilizing them as basic data for establishing tourism policies and marketing strategies.

I created this dataset by rebuilding the data provided by the Indian Government for easy analysis.

Column Name and Explanation:

noftaii: No. of Foreign Tourist Arrivals in India (in Million) noftaiiagr: No. of Foreign Tourist Arrivals in India, Annual growth rate(in %)(compare to the previous year) noindfi: No.of Indian Nationals departures from India (in Million) noindfiagr: No.of Indian Nationals departures from India, Annual growth rate(in %)(compare to the previous year) nodtvasu: No. of Domestic Tourist Visits to all States/UTs nodtvasuagr: No. of Domestic Tourist Visits to all States/UTs feeftit: Estimated Foreign Exchange Earnings from Tourism in INR terms in Crores feeftitagr: Estimated Foreign Exchange Earnings from Tourism in INR terms, Annual growth rate(in %)(compare to the previous year) feeftust: Estimated Foreign Exchange Earnings from Tourism in US$ terms in Billions feeftustagr : Estimated Foreign Exchange Earnings from Tourism in US$ terms, Annual growth rate(in %)(compare to the previous year) wnoita: world level No. of International Tourist Arrivals in Millions wnoitaagr: world level No. of International Tourist Arrivals, Annual growth rate(in %)(compare to the previous year) witr: world level International Tourism Receipts in US$ Billion witragr: world level International Tourism Receipts in US$ Billion, Annual growth rate(in %)(compare to the previous year) aprnoita: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million aprnoitaagr: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million, Annual growth rate(in %)(compare to the previous year) apfitr: In Asia and The Pacific Region, International Tourism Receipts in US$ Billion apritragr: In Asia and The Pacific Region, International Tourism Receiptsin US$ Billion, Annual growth rate(in %)(compare to the previous year) ipwiita: India’s Position in World, Share of India in International Tourist Arrivals(in %) ipwirwta: India’s Position in World, India’s rank in World Tourist Arrivals ipwsiitr: India’s Position in World, Share of India in International Tourism Receipts (US$ terms) (in %) ipwirwtr: Position in Asia & the Pacific Region, India’s rank in World Tourism Receipts ipaprita: Position in Asia & the Pacific Region, Share of India in International Tourist Arrivals(in %)

Acknowledgments

Thanks to the Indian Ministry of Tourismfor making the data available to the general public. For more details, you can refer: https://github.com/kachhadiyaraj15/india_tourism_2014_2020

Cover Photo

Photo from Adobe Stock https://stock.adobe.com/in/images/collage-of-india-historical-monuments-architectural-buildings-and-ruins-a-india-tour-and-travel-banner-of-notable-tourist-destinations/142044867

Description

This Dataset contains details of World Population by country. According to the worldometer, the current population of the world is 8.2 billion people. Highest populated country is India followed by China and USA.

Attribute Information

• Rank : Country Rank by Population.
• Country : Name of the Country.
• Population(2024) : Current Population of each Country.
• Yearly Change : Percentage Yearly Change in Population.
• Net Change : Net change in the Population.
• Density (P/Km²) : Population density (population per square km)
• Land Area(Km²) : Total land area of the Country.
• Migrants (net) : Total number of migrants.
• Fertility Rate : Fertility rate
• Median Age : Median age of the population
• Urban Pop % : Percentage of urban population
• World Share : Share to the word with population.

Acknowledgements

https://www.worldometers.info/world-population/population-by-country/

Image by Gerd Altmann from Pixabay

Context

This dataset provides country-level GDP (Gross Domestic Product) in current US dollars from 2000 to 2025, mapped to the seven classic continents (Asia, Africa, Europe, North America, South America, Australia, and Antarctica). It is designed to make global economic data easier to explore, compare, and visualize by combining both geographic and temporal dimensions.

GDP is one of the most widely used indicators to measure the size of an economy, its growth trends, and relative economic performance across regions.

Source

Data Provider: World Bank Open Data

Indicator Used: NY.GDP.MKTP.CD → GDP (current US$)

License: World Bank Dataset Terms of Use (aligned with CC BY 4.0)

Note: 2024–2025 values may be incomplete or missing for some countries, depending on World Bank publication updates.

Dataset Structure

Name of country → Country name

Continent → One of the 7 continents

2000–2025 → GDP values in current US$ (float, may contain missing values NaN)

Format: wide panel data (one row per country, one column per year).

Inspiration & Use Cases

This dataset was prepared to make economic analysis, visualization, and forecasting more accessible. It can be used for:

• Time-series forecasting (predicting GDP growth into the future)
• Cross-country comparisons (e.g., comparing GDP trends of India vs. USA vs. Brazil)
• Continent-level aggregation (summing GDP by continent per year)
• Data visualization (heatmaps, line charts, world choropleths)
• Machine Learning applications (e.g., clustering countries by GDP trajectory)

Citation

If you use this dataset, please cite:

Source: World Bank, World Development Indicators (NY.GDP.MKTP.CD). Licensed under the World Bank Terms of Use.

Description from official website

Department of Commerce
Export Import Data Bank
Import :: Country-wise all commodities
Dated: 30/4/2024
Values in US$ Million
Quantity in thousands
Sorted on HSCode

Country: CHINA P RP
* ITC HS Code of the Commodity is either dropped or re-allocated and the unit of the commodity may be changed from April 2023.