In 2023, India accounted for ***** percent of the world's population. India's share of the global population has remained constant, with a decline since 2019.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

As of 2019, about ** percent of households across India were segmented as belonging to the NCCS C category of consumers. Contrariwise only **** percent of the country's population fell under the NCCS E category that year. Between 2014 and 2019, the share of population classified as category A, B, and C consumers has grown tremendously, reflecting the trajectory of the booming middle class within the Indian economy.

The population of small mammals kept as pets in India amounted to over 65 thousand in the year 2023. The population is forecast to reach around 68.9 thousand by the end of year 2028. Some of the popular mammals kept as pets in India are hamsters, guinea pigs and mice and rabbits.

The internet penetration rate in India rose over ** percent in 2025, from about ** percent in 2015. Although these figures seem relatively low, it meant that more than half of the population of **** billion people had internet access that year. This also ranked the country second in the world in terms of active internet users. Internet availability and accessibility By 2021, the number of internet connections across the country tripled, with urban areas accounting for a higher density of connections than rural regions. Despite incredibly low internet prices, internet usage in India has yet to reach its full potential. Lack of awareness and a tangible gender gap lie at the heart of the matter, with affordable mobile handsets and mobile internet connections presenting only a partial solution. Reliance Jio was the popular choice among Indian internet subscribers, offering them wider coverage at cheap rates. Digital living Home to one of the largest bases of netizens in the world, India is abuzz with internet activities being carried out every moment of every day. From information and research to shopping and entertainment to living in smart homes, Indians have welcomed digital living with open arms. Among these, social media usage was one of the most common reasons for accessing the internet.

India: Percent of world population: The latest value from 2023 is 17.94 percent, a decline from 17.95 percent in 2022. In comparison, the world average is 0.51 percent, based on data from 196 countries. Historically, the average for India from 1960 to 2023 is 16.5 percent. The minimum value, 14.54 percent, was reached in 1960 while the maximum of 17.97 percent was recorded in 2014.

About this dataset

Context

This list ranks the 473 cities in the California by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the California by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California Indian Population: This tells us how much of the entire California Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample excludes Northeast states and remote islands. In addition, some districts in Assam, Bihar, Jammu and Kashmir, Jharkhand, and Uttar Pradesh were replaced because of security concerns. The excluded areas represent less than 10% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in India was 3,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

The population share with mobile internet access in India was forecast to continuously increase between 2024 and 2029 by in total ** percentage points. After the fifteenth consecutive increasing year, the mobile internet penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Bangladesh and Sri Lanka.

About this dataset

Context

This list ranks the 9 cities in the New Castle County, DE by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the New Castle County, DE by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New Castle County Indian Population: This tells us how much of the entire New Castle County, DE Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Rural: 25-29 data was reported at 180.200 NA in 2016. This records an increase from the previous number of 163.500 NA for 2015. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Rural: 25-29 data is updated yearly, averaging 163.700 NA from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 180.200 NA in 2016 and a record low of 154.200 NA in 2014. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Rural: 25-29 data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH007: Vital Statistics: Age Specific Fertility Rate: by Age Group.

About this dataset

Context

This list ranks the 333 cities in the Massachusetts by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the Massachusetts by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Massachusetts Indian Population: This tells us how much of the entire Massachusetts Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 21 counties in the New Jersey by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each county over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of county in the New Jersey by their Indian population, using the most recent ACS data available.
• County: The County for which the rank is shown in the previous column.
• Indian Population: The Indian population of the county is shown in this column.
• % of Total County Population: This shows what percentage of the total county population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New Jersey Indian Population: This tells us how much of the entire New Jersey Indian population lives in that county. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Urban: 25-29 data was reported at 139.100 NA in 2016. This records an increase from the previous number of 125.800 NA for 2015. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Urban: 25-29 data is updated yearly, averaging 129.600 NA from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 139.100 NA in 2016 and a record low of 123.900 NA in 2014. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: Urban: 25-29 data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH007: Vital Statistics: Age Specific Fertility Rate: by Age Group.

India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: 25-29 data was reported at 166.000 NA in 2016. This records an increase from the previous number of 150.300 NA for 2015. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: 25-29 data is updated yearly, averaging 153.400 NA from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 166.000 NA in 2016 and a record low of 143.700 NA in 2014. India Vital Statistics: Age Specific Fertility Rates: per 1000 Female Population: Age: 25-29 data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH007: Vital Statistics: Age Specific Fertility Rate: by Age Group.

Vital Statistics: Birth Rate: per 1000 Population: Bihar: Urban data was reported at 21.000 NA in 2020. This records a decrease from the previous number of 21.200 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Bihar: Urban data is updated yearly, averaging 22.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 25.600 NA in 2000 and a record low of 19.900 NA in 2014. Vital Statistics: Birth Rate: per 1000 Population: Bihar: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

India Vital Statistics: Age Specific Fertility Rate: per 1000 Female Population: Educational Level of Women: Literate: Secondary School: Age: 25-29 data was reported at 172.300 NA in 2016. This records an increase from the previous number of 139.600 NA for 2015. India Vital Statistics: Age Specific Fertility Rate: per 1000 Female Population: Educational Level of Women: Literate: Secondary School: Age: 25-29 data is updated yearly, averaging 138.100 NA from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 172.300 NA in 2016 and a record low of 133.600 NA in 2014. India Vital Statistics: Age Specific Fertility Rate: per 1000 Female Population: Educational Level of Women: Literate: Secondary School: Age: 25-29 data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH008: Vital Statistics: Age Specific Fertility Rate: by Education Level of Women.

Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar data was reported at 20.000 NA in 2020. This records a decrease from the previous number of 20.200 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar data is updated yearly, averaging 21.500 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 23.100 NA in 2000 and a record low of 19.700 NA in 2014. Vital Statistics: Natural Growth Rate: per 1000 Population: Bihar data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH004: Vital Statistics: Natural Growth Rate: by States.

This statistic presents information on the online video audience demographics in India as of July 2014. During the survey period it was found that **** million video viewers in India were men aged between 15 and 24 years.

Context

This dataset contains information about Indian School Education Statistics of the year 2013-2014 to 2015-2016. Many public datasets from Indian Government are scattered and the goal here is to have all those datasets under one umbrella so that it is easy for beginners to find important datasets like this to start their Data Science journey.

Content

I acquired this dataset from here. Have a look at the website.

This dataset contains 7 files in .csv format. You can find a description for each column. Let me summarize it here too. 1. Percentage of Schools with Drinking Water Facility from 2013-14 to 2015-16 2. Gross Enrolment Ratio from 2013-14 to 2015-16 3. Drop-out rate from 2012-13 to 2014-15 4. Percentage of Schools with Computers from 2013-14 to 2015-16 5. Percentage of Schools with Electricity from 2013-14 to 2015-16 6. Schools with Boys Toilet from 2013-14 to 2015-16 7. Schools with Girls Toilet from 2013-14 to 2015-16

Inspiration

This dataset is especially useful for starters in their Data Science journey, also whoever is interested in knowing how the education in India is progressing over the years. You can start your exploration by answering the below questions:

• Which states have the highest Dropout Ratio?
• Do boys and girls have enough water and toilet facilities available?
• How is the Gross Enrollment ration for boys and girls in various levels of schooling life?
• Which level of school life is there less number of enrollment? Is it dropping or increasing?
• Any other ideas.

Acknowledgements

I thank the Indian Government for making it public under their Open Government Data (OGD) Platform India.