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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Context
The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.
Key observations
The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for United States Population by Age. You can refer the same here
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TwitterWhat'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. This dataset contains over 48,000 rows of data based on US census data. This dataset is used to determine whether an adult's income exceeds $50K/yr based on 14 different attributes collected. The dataset contains a distribution of 23.93% entries labelled with >50k and 76.07% entries labeled with < with <=50k. The attributes are as follows:
age workclass (eg. private, local-gov, never-worked...) fnlwgt (the number of people the census believes the entry represents) education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native country label (whether the individual makes more than $50,000 annually)
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Boston population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Boston. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.
Key observations
The largest age group was 18 - 64 years with a poulation of 487,505 (72.46% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age cohorts:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Boston Population by Age. You can refer the same here
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the American Falls population by age. The dataset can be utilized to understand the age distribution and demographics of American Falls.
The dataset constitues the following three datasets
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.
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/.
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TwitterA survey carried out in 2021 revealed how important having more control over personal data is to adults in the United States. A third of respondents said this was extremely important to them, another third said it was very important, and roughly ** percent said it was moderately important. Just *** percent said having more control over personal data and how it is shared was not at all important to them. Most Americans worry about falling victim to cybercrime When asked in a 2020 survey to name crimes they were most afraid of, the majority of American respondents claimed to be more worried about cybercrimes than having their home broken into or car stolen. The top two concerns were having personal financial information stolen by hackers, and being the victim of identity theft. This is unsurprising, considering the rise in data breaches and exposed records over the past decade. In the United States alone, *** billion data breaches were reported in 2017 - the highest figure to date. It is likely that this figure was somewhat impacted by the infamous Yahoo! data breach scandal that year, where * billion user accounts worldwide were compromised. This has been recognized as one of the largest breaches of its kind in history. IT security market on the rise As a direct result of increasing security demands and rising concerns, more companies than ever before are investing in cyber security, both in the form of services and technology. The information security technology market is booming, and as a result worldwide spend is predicted to hit nearly *** billion U.S. dollars by 2024, a ** billion dollar increase from one year prior.
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TwitterAs of May 2024, 15 percent of adults in the United States said they were very willing to pay a premium fee to companies with strong data protection, while 45 percent of adults said they were somewhat willing. In Comparison, 18 percent of U.S. adults said they were very unwilling to pay for extra personal data protection.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the American Fork population by age. The dataset can be utilized to understand the age distribution and demographics of American Fork.
The dataset constitues the following three datasets
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.
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/.
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TwitterThis dataset has been taken from the famous UCI Machine Learning Repository. The goal of this notebook is to accurately predict whether or not an adult makes more than 50000 US Dollars in an year on the basis of the features given. Age: Describes the age of individuals. Continuous. fnlwgt: Continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. education-num: Number of years spent in education. Continuous. sex: Female, Male. capital-gain: Continuous. capital-loss: Continuous. hours-per-week: Continuous. salary: >50K,<=50K
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TwitterAs of May 2024, 33 percent of adults in the United States reported having their personal data breached, this represents a 13 percent increase in comparison to the previous year. Approximately 42 percent of respondents stated that they do not believe their data was exposed, while 25 percent of respondents said they do not know if their data was breached.
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TwitterThe Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. I encountered it during my course, and I wish to share it here because it is a good starter example for data pre-processing and machine learning practices.
Fields
The dataset contains 16 columns Target filed: Income -- The income is divide into two classes: 50K Number of attributes: 14 -- These are the demographics and other features to describe a person
We can explore the possibility in predicting income level based on the individual’s personal information.
Acknowledgements
This dataset named “adult” is found in the UCI machine learning repository
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TwitterA survey conducted in the United States, between July and October 2024, found that the highest percentage of consumers were worried about social media platforms using their personal data. Additionally, the survey unveiled that more users were worried that the U.S. government could use their personal data, rather than foreign governments. Search engines and internet browsers ranked high up among third parties, triggering concerns of personal data use.
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TwitterFacebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.
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Twitterhttps://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required
Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 15 to 64 Years for United States (LFWA64TTUSM647S) from Jan 1977 to Aug 2025 about working-age, 15 to 64 years, population, and USA.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Florida population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Florida. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.
Key observations
The largest age group was 18 to 64 years with a poulation of 12.99 million (59.25% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age cohorts:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Florida Population by Age. You can refer the same here
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TwitterBy Health [source]
The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.
The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.
Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.
- Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
- Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
- Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc
If you use this dataset in your research, please credit the original authors. Data Source
See the dataset description for more information.
File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...
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TwitterThis dataset includes select data from the U.S. Census Bureau's American Community Survey (ACS) on the percent of adults who bike or walk to work. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about ACS visit https://www.census.gov/programs-surveys/acs/.
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TwitterBy US Open Data Portal, data.gov [source]
This dataset contains stroke mortality data among US adults (35+) by state/territory and county. Learn more about the health of people within your own state or region, across genders and ethnicities. Reliable statistics even for small counties can be seen, thanks to 3-year averages, age-standardization, and spatial smoothing. Data sources such as the National Vital Statistics System give you all the data you need to get a detailed sense of your population's total cardiovascular health. With interactive maps created from this data also provided covering heart disease risks, death rates and hospital bed availability across each location in America, you can now gain a powerful perspective on how effective healthcare initiatives are making an impact in those who live there. Study up on the real cardiovascular conditions plaguing those around us today to make a real change in public health!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
This dataset contains stroke mortality data among US adults (35+) by state/territory and county. This data can be useful in helping identify areas where stroke mortality is high, and interventions to reduce mortality should be taken into account.
To access the dataset, you need to download it from Kaggle. The dataset consists of 18 columns including year, location description, geographic level, source of data, class of data values provided, topic of discussion with regard to stroke mortality rates (age-standardized), labels for stratification categories and stratifications used within the given age group when performing this analysis. The last 3 columns consist of geographical coordinates for each location (Y_lat & X_lon) as well as an overall georeferenced column (Georeferenced Column).
Once you have downloaded the dataset there are a few ways you can go about using it:
- You can perform a descriptive analysis on any particular column using methods such as summary statistics or distributions graphs;
- You can create your own maps or other visual representation based on the latitude/longitude columns;
- You could look at differences between states and counties/areas within states by subsetting out certain areas;
- Using statistical testing methods you could create inferential analyses that may lead to insights on why some areas seem more prone to higher levels of stroke mortality than others
- Track county-level stroke mortality trends among US adults (35+) over time.
- Identify regions of higher stroke mortality risk and use that information to inform targeted, preventative health policies and interventions.
- Analyze differences in stroke mortality rates by gender, race/ethnicity, or geographic location to identify potential disparities in care access or outcomes for certain demographic groups
If you use this dataset in your research, please credit the original authors. Data Source
Unknown License - Please check the dataset description for more information.
File: csv-1.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------| | Year | Year of the data. (Integer) | | LocationAbbr | Abbreviation of the state or territory. (String) | | LocationDesc | Name of the state or territory. (String) | | GeographicLevel | Level of geographic detail. (String) | | DataSource | Source of the data. (String) | | Class | Classification of the data. (String) | | Topic | Topic of the data. (String) | | Data_Value | Numeric value associated with the topic. (Float) | | Data_Value_Unit | Unit used to express the data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Symbol associated with the data value footnote. (String) | | StratificationCategory1 | First category of stratification. (String) | | Stratification1 | First stratifica...
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Norwood Young America population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Norwood Young America. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.
Key observations
The largest age group was 18 - 64 years with a poulation of 2,370 (61.75% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age cohorts:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Norwood Young America Population by Age. You can refer the same here
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TwitterThe 2017 National Financial Well-Being in America Survey, conducted for the CFPB Offices of Financial Education and Financial Protection for Older Americans, was an online survey conducted to measure the financial well-being of adults in the United States. These data were created as a foundation for internal and external research into financial well-being and are relevant to work being done by researchers in the Office of Research who have access to the (deidentified) data.
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TwitterThis is the US Census Income data set:
- the original data from UCI Edu.
- where adult.data & adult.test = 48K rows, different than 32K rows that has been mainly circulated around
- which also comes with many other challenges for data wrangling
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.
Key observations
The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for United States Population by Age. You can refer the same here