This is a Dataset of the World Population Consisting of Each and Every Country. I have attempted to analyze the same data to bring some insights out of it. The dataset consists of 234 rows and 17 columns. I will analyze the same data and bring the below pieces of information regarding the same.

1. Continent Population Characteristics Analysis.
2. Analysis of Countries.
   • Top 10 Most Populated and Least Populated Countries
   • Top 10 Largest and Smallest Countries as per Area
   • Population Growth From 1970 to 2020 (50 Years)
3. Countries Represent % Of World Population.
   • Countries that represent below 0.1% of the World Population.
   • Countries that represent above 2% of the world Population
   • Top 10 Over Populated Countries based on Density Per Sq KM.
   • Top 10 Least Populated Countries based on Density Per Sq KM.

This dataset contains carbon fluxes for the 10 largest countries in the world (here EU27 is treated as a country) using GOSAT and OCO-2 observational constraints for 2017-2019.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

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

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.

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 World's Top Military Strength Dataset is a comprehensive compilation of data gathered from various reliable sources, providing a detailed analysis and comparison of the military capabilities of countries worldwide. This dataset serves as a valuable resource for policymakers, researchers, defense analysts, and military enthusiasts, enabling them to assess and understand the relative strength and capacities of different nations' armed forces.

The dataset encompasses a wide range of parameters that are crucial in evaluating military power. It includes both quantitative and qualitative metrics, capturing factors such as defense budget, personnel strength, equipment inventory, technological advancements, research and development investments, logistical capabilities, and more. By incorporating multiple dimensions, the dataset offers a comprehensive view of a country's military prowess.

Data within the dataset has been meticulously scraped and curated from reputable websites, government publications, defense journals, and international reports. Rigorous efforts have been made to ensure data accuracy and consistency, minimizing errors and biases to provide users with reliable information.

Connect with our experts for Street and Venue Noise-Level Data. Unlock unique insights into the real-world acoustic environment of cities and venues across 180+ countries. Silencio has built the world’s largest database on noise levels, statistically interpolated using over 35 billion datapoints, developed in collaboration with leading acoustics professionals. Unlike traditional models that rely solely on computed estimations, our dataset uniquely combines real-world measurements with AI-driven predictions to deliver the most accurate and reliable noise-level data available today.

Maximize AI Performance with the World’s Largest Real-World Noise-Level Dataset

What sets our dataset apart? Silencio’s Street and Venue Noise-Level Data is the world’s largest and most accurate collection of real-world acoustic data, combining over 35 billion datapoints with AI-driven interpolation, developed together with professional acousticians. Unlike synthetic models, our dataset integrates real measurements and AI predictions to provide unparalleled ground truth for AI training.

Designed for AI Applications: Empower your AI models with high-quality, diverse, and realistic acoustic data. Ideal for training AI in sound recognition, noise mapping, autonomous systems, smart cities, mobility intelligence, and beyond.

Reliable & Compliant: Collected through our mobile app with explicit user consent, fully anonymized, and fully GDPR-compliant, ensuring ethical sourcing and regulatory alignment.

Historical & Real-Time: Train models using both historical and continuously updated data to improve accuracy and robustness over time and across regions.

Granular & Customizable: Globally available, highly granular, and adaptable to your AI pipeline needs — from raw acoustic datapoints to aggregated sound profiles.

Simple Integration: Delivered via CSV exports or S3 bucket delivery (APIs coming soon), allowing smooth integration into your existing AI training workflows.

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

Street Noise-Level Dataset

Silencio’s Street Noise-Level Dataset offers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows buyers to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

Context

This project analyzes the 2020 World Happiness Report to draw conclusions about the general well being of Africa. It uses several CSV files consisting of survey responses formed from a Google Form survey, data from the 2020 World Happiness Report and data on countries only in Africa from the 2020 World Happiness Report. The main data set used includes over 150 countries and their happiness scores, freedom to make life choices, social support, healthy life expectancy, regional indicator, perceptions of corruption and generosity. This analysis was done to answer the following data-driven questions: 'Which African country ranked the happiest in 2020?' and 'Which variable predicts or explains Africa's happiness score?'

This project includes several programs created in R and Python.

Background

The Gallup World Poll (GWP) is conducted annually to measure and track public attitudes concerning political, social and economic issues, including controversial and sensitive subjects. Annually, this poll tracks attitudes toward law and order, institutions and infrastructure, jobs, well-being and other topics for approximately 150 countries worldwide. The data gathered from the GWP is used to create an annual World Happiness Report (WHR). The World Happiness Report is conducted to review the science of understanding and measuring the subjective well-being and to use survey measures of life satisfaction to track the quality of lives in over 150 countries.

At first glance, it seems that world happiness isn't important or maybe it's just an emotional thing. However, several governments have started to look at happiness as a metric to measure success. Happiness Scores or Subjective Well-being (SWB) are national average responses to questions of life evaluation. They are important because they remind policy makers and people in power that happiness is based on social capital, not just financial. Happiness is often considered an essential and useful way to guide public policies and measure their effectiveness. It is also important to note that happiness scores point out the importance of qualitative rather than quantitative. At times, quality is better than quantity.

Africa is the world's second largest and second most populous continent in the world. It consists of 54 countries meaning that Africa has the most countries. Africa has approximately 30% of the earth's mineral resources and has the largest reserves of precious metals. Africa reserves over 40% of the gold reserves, 60% on cobalt and 90% of platinum. However, Africa unfortunately has the most developmental challenges. It is the world's poorest and most underdeveloped continent. Africa is also almost 100% colonized with the exceptions of Ethiopia and Liberia. Given this information, one can wonder what the SWB or state of happiness is in Africa?

This site analyzes the 2020 World Happiness Report to draw conclusions to data-drive questions listed later on this page. The focus is specifically on countries in Africa. Even though there are 54 countries in Africa, only 43 participated in the 2020 WHR.

Content

The dataset used is generated from the 'World Happiness Report 2020'. This dataset contains the Happiness Score for over 150 countries for the year of 2020. The data gathered from the Gallup World Poll gives a national average of Happiness scores for countries all over the world. It is a annual landmark survey of the state of global happiness.

This dataset is from the data repository "Kaggle". On Kaggle's dataset page, I searched for Africa Happiness after filtering the search to CSV file type. I wasn't able to find any datasets that could answer my questions that didn't include other countries from different continents. I decided to use a Global Happiness Report to answer the questions I have. The dataset I am using was publish by Micheal Londeen and it was created on March 24, 2020. His main source is the World Happiness Report for 2020.

Variables

Happiness score or subjective well-being (variable name ladder ): The survey measure of SWB is from the Feb 28, 2020 release of the Gallup World Poll (GWP) covering years from 2005 to 2019. Unless stated otherwise, it is the national average response to the question of life evaluations. The English wording of the question is “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” This measure is also referred to as Cantril life ladder, or just life ladder in our analysis.

Healthy Life Expectancy (HLE). Healthy life expectancies at birth are based on the data extracted from the World Health Organization’s (WHO) Global Health Observatory dat...

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

KU herpetology houses one of the largest herpetology collections in the world (340,000 specimens representing more than 5000 species from 156 countries). The KU collections include the world’s largest collection of neotropical amphibian and reptile specimens (200,000+) as well as substantial numbers of Nearctic (80,000+) and Asian (20,000+) specimens. KU holdings are particularly strong for the U.S., Ecuador, Mexico, the Dominican Republic, Costa, Rica, Haiti, the Philippines, Peru and Panama. The collection from Kansas is the state’s largest (20,000+). The type collection includes nearly 400 primary types, mostly amphibians. KU Herpetology also maintains 5000 cleared-and-stained osteological preparations, nearly 5000 dried skeletons, and one of the world’s largest collections of amphibian larvae (6000+ lots). The KU digital archive includes more than 12,000 digital images and more than 1500 acoustic recordings.

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

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

Techsalerator’s Import/Export Trade Data for North America

Techsalerator’s Import/Export Trade Data for North America delivers an exhaustive and nuanced analysis of trade activities across the North American continent. This extensive dataset provides detailed insights into import and export transactions involving companies across various sectors within North America.

Coverage Across All North American Countries

The dataset encompasses all key countries within North America, including:

1. United States

The dataset provides detailed trade information for the United States, the largest economy in the region. It includes extensive data on trade volumes, product categories, and the key trading partners of the U.S. 2. Canada

Data for Canada covers a wide range of trade activities, including import and export transactions, product classifications, and trade relationships with major global and regional partners. 3. Mexico

Comprehensive data for Mexico includes detailed records on its trade activities, including exports and imports, key sectors, and trade agreements affecting its trade dynamics. 4. Central American Countries:

Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama The dataset covers these countries with information on their trade flows, key products, and trade relations with North American and international partners. 5. Caribbean Countries:

Bahamas Barbados Cuba Dominica Dominican Republic Grenada Haiti Jamaica Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago Trade data for these Caribbean nations includes detailed transaction records, sector-specific trade information, and their interactions with North American trade partners. Comprehensive Data Features

Transaction Details: The dataset includes precise details on each trade transaction, such as product descriptions, quantities, values, and dates. This allows for an accurate understanding of trade flows and patterns across North America.

Company Information: It provides data on companies involved in trade, including names, locations, and industry sectors, enabling targeted business analysis and competitive intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, offering insights into market dynamics and sector-specific trends within North America.

Trade Trends: Historical data helps users analyze trends over time, identify emerging markets, and assess the impact of economic or political events on trade flows in the region.

Geographical Insights: The data offers insights into regional trade flows and cross-border dynamics between North American countries and their global trade partners, including significant international trade relationships.

Regulatory and Compliance Data: Information on trade regulations, tariffs, and compliance requirements is included, helping businesses navigate the complex regulatory environments within North America.

Applications and Benefits

Market Research: Companies can leverage the data to discover new market opportunities, analyze competitive landscapes, and understand demand for specific products across North American countries.

Strategic Planning: Insights from the data enable companies to refine trade strategies, optimize supply chains, and manage risks associated with international trade in North America.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development strategies.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in North America's diverse economies.

Techsalerator’s Import/Export Trade Data for North America offers a vital resource for organizations involved in international trade, providing a thorough, reliable, and detailed view of trade activities across the continent.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

The Southern Ocean is a remote, hostile environment where conducting marine biology is challenging, so we know relatively little about this important region, which is critical as a habitat for breeding and foraging of many marine endotherms. Scientists from around the world have been tracking seals, penguins, petrels, whales and albatrosses for more than two decades to learn how they spend their time at sea. The Retrospective Analysis of Antarctic Tracking Data (RAATD), was initiated by the SCAR Expert Group on Marine Mammals (EG-BAMM) in 2010. This team has assembled tracking data shared by 38 biologists from 11 different countries to accumulate the largest animal tracking database in the world, containing information from 15 species, containing over 3,400 individual animals and almost 2.5 million at-sea locations. Analysing a dataset of this size brings its own challenges and the team is developing new and innovative statistical approaches to integrate these complex data. When complete RAATD will provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, help predict the future of top predator distribution and help with spatial management planning.

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

This data publication is a new compilation of land gravity data expressing the Earth’s gravitational acceleration field on the broader area of the European Alps. The dataset is based on national databases from 10 countries, but surmounts any barriers related to national reference systems. The input to this dataset is the largest Alpine compilation of point-wise data on land ever, and also includes marine data in adjacent regions in the Mediterranean Sea. Following quality control, this represents a total of 349’938 terrestrial gravity points and about 700’000 marine stations. Only such a dataset allows to investigate the Alpine orogen from shallow (sedimentary) to large (mantle) depths, which is among the primary goals of the AlpArray science program. Broad effort to collect all existing, public and private, point-based gravity data in the area of interest: 2-23°E, 41-51°N. The final, published grids are shared with the community on a 4*4 km2 grid; the results on 2*2 km2 grid are available upon request and approval from the group. We developed and fine-tuned an approach in which all raw data could be processed in the same, homogeneous way. Outliers were discarded. Full details are given in the reference publication (Zahorec et al., 2020).