Factori houses an extensive dataset of US People data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US People Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our People data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US People data Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

Factori's People Data API empowers businesses to enhance their contact database of Shopify and Klaviyo by enriching data. Simply input phone numbers, email addresses, hashed values, or name/company details, and receive comprehensive contact details in a standardized format. Fuel your marketing, sales, and customer relationship management activities with enriched contact information, including names, company details, job titles, contact information, social media profiles, and more. With optimized performance, robust error handling, and data security measures, Factori's People Data API provides a seamless experience. Unlock valuable insights, personalize your outreach, and drive business growth effortlessly with Factori's People Data API. Use Cases: Personalized Marketing: Enrich existing contact data with additional details such as social media profiles, educational background, or job titles. Tailor your marketing messages and campaigns to specific customer segments, improving personalization and engagement. Account-Based Marketing (ABM): Enhance your ABM strategy by enriching contact data of target accounts. Gain a comprehensive understanding of key stakeholders, their roles, and their preferences to deliver highly targeted and personalized campaigns. Sales Intelligence: Arm your sales team with enriched contact information to improve prospecting and sales conversations. Access valuable insights such as past experiences, interests, or industry expertise to establish meaningful connections and drive conversions. Data Cleansing and Validation: Ensure the accuracy and completeness of your contact database by enriching existing data with verified information. Update outdated or missing contact details, improving data quality and integrity. Market Research and Analysis: Enrich contact data to gain deeper insights into industry trends, job movements, or market dynamics. Analyze enriched data to identify patterns, opportunities, and market gaps for informed decision-making.

People Data Labs is an aggregator of B2B person and company data. We source our globally compliant person dataset via our "Data Union".

The "Data Union" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Some data sources are provided as a one time dump, others are refreshed every time we do a new data build. Our data sources come from a variety of verticals including HR Tech, Real Estate Tech, Identity/Anti-Fraud, Martech, and others. People Data Labs works with customers on compliance based topics. If a customer wishes to ensure anonymity, we work with them to anonymize the data.

Our company data has identifying information (name, website, social profiles), company attributes (industry, size, founded date), and tags + free text that is useful for segmentation.

Factori is a compliant, flexible, and adaptable data provider. We help you make smarter decisions, fill all the gaps in your data, uncover patterns, gain a competitive advantage, and build better solutions by bringing accurate, holistic, privacy-compliant global consumer data.

We specialize in building the world’s largest consumer graph that ingests, de-dupes, and transforms premium data from over 2.3 billion anonymous customer profiles with 800+ attributes, which powers insights for smarter decision-making and building adequate solutions. We take privacy and personal information very seriously and are committed to adhering to all applicable data privacy and security laws and regulations, including the GDPR, CCPA, and ISO 27001.

In the dynamic realm of business, the perpetual challenge of maintaining current customer data is ever-present. Factori’s People Data API efficiently manages the ingestion, deduplication, and transformation of premium data sources, saving you valuable time and effort.

With our API, you can access and utilize subsets of our comprehensive person dataset, empowering you to gain actionable intelligence, make data-driven decisions, and build innovative products and services. Whether you're a marketer, data scientist, or business analyst, our US People Data can unlock new opportunities for your organization.

Designed as a comprehensive data enrichment solution, our US People database fills gaps in your customer data, offering profound insights into your consumers. Encompassing over 300 million profiles with more than 40 variables spanning location, demographics, lifestyle, hobbies, and behaviors, it acts as a guiding compass for understanding your customers' past, present, and potential future behaviors. This enables you to navigate the business landscape with clarity, making decisions grounded in comprehensive and informed perspectives.

Here are some of the data categories and attributes we offer within the US People Data Graph: Geography: City, State, ZIP, County, CBSA, Census Tract, etc. Demographics: Gender, Age Group, Marital Status, Language, etc. Financial: Income Range, Credit Rating Range, Credit Type,etc. Persona: Consumer type, Communication preferences, Family type, etc. Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. Household: Number of Children, Number of Adults, IP Address, etc. Behaviors: Brand Affinity, App Usage, Web Browsing, etc. Firmographics: Industry, Company, Occupation, Revenue, etc. Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc. Auto: Car Make, Model, Type, Year, etc. Housing: Home type, Home value, Renter/Owner, Year Built, etc.

Use Cases: Sales Intelligence: Precision Market Analysis and Segmentation Engage with personalized campaigns Enhance Lead Scoring and Qualification Strategic Marketing: Precision Market Analysis and Segmentation Engage with personalized campaigns Enhance Lead Scoring and Qualification Fraud and Cybersecurity: Unlock comprehensive identity insights Seamless KYC Compliances. Real-time Threat Detection HR Tech: Elevate Candidate Profiles Forge Talent Pathways Track role transitions

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

Dataset

This dataset was created by Ron Nahshon

Released under CC0: Public Domain

Contents

What is the Seeking Alpha API? Seeking Alpha API from RapidAPI is an API that queries stock news, market-moving, price quotes, charts, indices, analysis, and many more from investors and experts on seeking alpha stock research platform. In addition, it has a comprehensive list of endpoints for different categories of data.

Currently, the API has three pricing plans and a free subscription. It supports various programming languages, including Python, PHP, Ruby, and Javascript. This article will dig deeper into its details and see how to use this API with multiple programming languages.

How does the Seeking Alpha API work? Seeking Alpha API works using simple API logic in which It sends a request to a specific endpoint and obtains the necessary output as the response. When sending a request, it includes x-RapidAPI-key and host as authentication parameters so that the server can identify it as a valid request. In addition, the API requests body contains the optional parameters to process the request. Once the API server has received the request, it will process the request using the back-end application. Finally, the server will send back the information requested by the client in JSON format.

Target Audience for the Seeking Alpha API Financial Application Developers Financial application developers can integrate this API to attract Seeking Alphas’ audience to their financial applications. Its comprehensive list of APIs enables providing the complete Seeking Alpha experience. This API has affordable pricing plans, each endpoint requires only a few lines of code, and integration to an application is pretty straightforward. Since it supports multiple programming languages, it has widespread usability.

Stock Market Investors and learners Investors, especially those who research financial companies and the stock market, can use this to get information straight from this API. In addition, it has a free plan, and its Pro plan only costs $10. Therefore, anyone who learns about the stock market can make use of it for a low cost.

How to connect to the Seeking Alpha API Tutorial – Step by Step Step 1 – Sign up and Get a RapidAPI Account. RapidAPI is the world’s largest API marketplace which is used by more than a million developers worldwide. You can use RapidAPI to search and connect to thousands of APIs using a single SDK, API key, and Dashboard.

To create a RapidAPI account, go to rapidapi.com and click on the Sign Up icon. You can use your Google, Github, or Facebook account for Single Sign-on (SSO) or create an account manually.

MyAnimeList API Download

This is the dataset that I created as part of the Google Data Analytics Professional Certificate capstone project. The MyAnimeList website has a vast repository of ratings and rankings of viewership data that could be used for various methods. I extracted several datasets from the detail API from MyAnimeList (MAL) https://myanimelist.net/apiconfig/references/api/v2 and plan to potentially update data every two weeks.

Many possible uses for this data could be tracking what anime viewers are watching most within a particular time period, what's being scored (out of 10) well and what isn't.

My viz for this data will be part of a tableau dashboard located here. This dashboard allows fans to explore the dataset and locate top scored or popular titles by genre, time period, and demographic (although this field isn't always entered)

Documentation

The extraction and cleaning process is outlined on github here.

Frequency of Updates

I plan on updating this potentially every 2 weeks, this depends on my availability and the interest in this dataset.

Caveats

Extracting and loading this data involved some transformations that should be noted:

• This data only includes titles that correspond with the "tv" ranking category. This was in an effort to streamline extraction and fine tune the analysis. If you would like to see other categories you are welcome to suggest it as an enhancement or use the code create your own dataset. As a result of subsetting on "tv", the dataset excludes the following ranking categories:
  1. All
  2. airing
  3. upcoming
  4. ova
  5. movie
  6. special
  7. bypopularity
  8. favorite
• Adult content - This extract excludes all adult content (r+).
• Note: The previous two points are valid for all tables with the exception of the rank_table. This is the table that was used as a starting point to obtain all MAL ids that were associated with "tv". Because this is a fast download, all categories are included in this table.
• The creation of the alternative_title field in the anime_table. This uses the english version of the name unless it is null, if the value is null, it uses the default name. This was in an effort to make the title accessible to english speakers. The original title field can be used if desired.
• The extraction of the demographic information from the genres field. MyAnimeList includes demographic information (shounen, seinen etc.) in the genres field. I've extracted it so that it could be used as its own field. However, many of those fields are null making it somewhat difficult to use.
• Cleaning processes of data. Various methods of cleaning data have been carried out and are noted on github.
• start_season.year - this field in the anime_table has been modified for null values. If there are null values, the first four characters from the start_date have been used. I will continue to use this method as long as it is viable.

Table Structure

The primary keys in all of the tables (with the exclusion of the tm_ky table) are foreign keys to other tables. As a result, the tables have 2 or more primary keys.

1. anime_demo_table

1. anime_genres_table

1. anime_ranking_table

1. anime_studios_table

1. anime_syn_table

1. anime_table

This dataset provides the whole set of OECD annual employment and population data and is recommended for users who wish to query a large amount of data. It is not designed for visualising results using the Table and Chart buttons. To access the ‘Developer API query builder’, click on the ‘Developer API’ button above.

The application programming interface (API), based on the SDMX standard, allows a developer to access the data using simple RESTful URL and HTTP header options for various choices of response formats including JSON. The query filter is generated according to the current data selection. To change the data selection, use the filters on the left.

   To get started check the <a href="https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf">API documentation</a> <br>.For any question <a href="https://www.oecd.org/contact/">contact us</a>

Context

The CDC keeps a register of clinical trials. This dataset contains all clinical trials relating to Covid-19 that are stored in the CDC's register.

Content

Fields include study name, interventions being tested, the study type, and the status of the study. Please comment here or on the Notebook if you would like more fields added.

Acknowledgements

Thank you to @savannareid for pointing me towards the website for this data.

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

People Trend Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This API is designed to find the rankings by geography within the state for a specific metric (population or household) and rank (any of the metrics from provider, demographic, technology or speed). The results are the top ten and bottom ten records within the state for the particular geography type and my area rankings. Additionally we include +/- 5 rankings from the 'my' area rank.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Clark Street Mercantile on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Factori houses an extensive dataset of US People data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US People Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our People data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US People Data Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

Dataset

This dataset was created by MoyassarEltigani

Released under CC0: Public Domain

Contents

People S Bank Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

A broad and generalized selection of 2014-2018 US Census Bureau 2018 5-year American Community Survey population data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of total population, male and female, and both broad and narrowly-defined age groups. In addition to the standard selection of age-group breakdowns (by male or female), the dataset provides supplemental calculated fields which combine several attributes into one (for example, the total population of persons under 18, or the number of females over 65 years of age). The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Dataset

This dataset was created by Vimal Pillai

Released under CC0: Public Domain

Contents

Table of INEBase Year 2021: Summary of People data by gender, demographic characteristics and type of ICT use. National. Survey on Equipment and Use of Information and Communication Technologies in Households