Context

Similar to others who have created HR data sets, we felt that the lack of data out there for HR was limiting. It is very hard for someone to test new systems or learn People Analytics in the HR space. The only dataset most HR practitioners have is their real employee data and there are a lot of reasons why you would not want to use that when experimenting. We hope that by providing this dataset with an evergrowing variation of data points, others can learn and grow their HR data analytics and systems knowledge.

Some example test cases where someone might use this dataset:

HR Technology Testing and Mock-Ups Engagement survey tools HCM tools BI Tools Learning To Code For People Analytics Python/R/SQL HR Tech and People Analytics Educational Courses/Tools

Content

The core data CompanyData.txt has the basic demographic data about a worker. We treat this as the core data that you can join future data sets to.

Please read the Readme.md for additional information about this along with the Changelog for additional updates as they are made.

Acknowledgements

Initial names, addresses, and ages were generated using FakenameGenerator.com. All additional details including Job, compensation, and additional data sets were created by the Koluit team using random generation in Excel.

Inspiration

Our hope is this data is used in the HR or Research space to experiment and learn using HR data. Some examples that we hope this data will be used are listed above.

Contact Us

Have any suggestions for additions to the data? See any issues with our data? Want to use it for your project? Please reach out to us! https://koluit.com/ ryan@koluit.com

HR analytics, also referred to as people analytics, workforce analytics, or talent analytics, involves gathering together, analyzing, and reporting HR data. It is the collection and application of talent data to improve critical talent and business outcomes. It enables your organization to measure the impact of a range of HR metrics on overall business performance and make decisions based on data. They are primarily responsible for interpreting and analyzing vast datasets.

Download the data CSV files here ; https://drive.google.com/drive/folders/18mQalCEyZypeV8TJeP3SME_R6qsCS2Og

• The HR dataset is a collection of employee data that includes information on various factors that may impact employee performance. To explore the employee performance factors using Python, we begin by importing the necessary libraries such as Pandas, NumPy, and Matplotlib, then load the HR dataset into a Pandas DataFrame and perform basic data cleaning and preprocessing steps such as handling missing values and checking for duplicates.

• The dataset also use various data visualization to explore the relationships between different variables and employee performance. For example, scatterplots to examine the relationship between job satisfaction and performance ratings, or bar charts to compare the average performance ratings across different gender or positions.

In 2023, there were approximately ******* human resources workers in the United States. This was a significant increase since 2020 as numbers have returned to pre-pandemic levels and beyond.

This dataset titled Human Resources.csv contains anonymized employee data collected for internal HR analysis and research purposes. It includes fields such as employee ID, department, gender, age, job role, and employment status. The data can be used for workforce trend analysis, HR benchmarking, diversity studies, and training models in human resource analytics.The file is provided in CSV format (3.05 MB) and adheres to general data privacy standards, with no personally identifiable information (PII).Last updated: April 11, 2025. Uploaded by Anurag Pardiash.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Information supporting the management, payment and benefits of FSA personnel.

At a bustling job fair in early 2025, a midsized tech startup stood out, not for its swag bags or giveaways, but for something less flashy and far more transformative. It had no recruiters on site. Instead, a sleek interface on kiosks allowed candidates to interact with an AI recruiter...

In 2023, almost ** percent of human resources managers in the United States were men. In 2022, there were approximately ******* human resources managers in the United States.

This is a dataset that pertains to human resources and employee data, with the following columns:

Attrition: A binary column indicating whether the employee has left the company or not. Business Travel: A categorical column indicating the frequency of business travel by the employee. CF_age band: A categorical column indicating the age range of the employee. CF_attrition label: A categorical column indicating the reason for the employee's attrition. Department: A categorical column indicating the department the employee belongs to. Education Field: A categorical column indicating the field of education of the employee. emp no: An integer column indicating the employee number. Employee Number: An integer column indicating the employee number. Gender: A categorical column indicating the gender of the employee. Job Role: A categorical column indicating the role or position of the employee. Marital Status: A categorical column indicating the marital status of the employee. Over Time: A binary column indicating whether the employee works overtime or not. Over18: A binary column indicating whether the employee is over 18 years old or not. Training Times Last Year: An integer column indicating the number of training sessions attended by the employee in the last year. Age: An integer column indicating the age of the employee. CF_current Employee: A binary column indicating whether the employee is currently employed or not. Daily Rate: An integer column indicating the daily rate of pay for the employee. Distance From Home: An integer column indicating the distance between the employee's home and workplace. Education: A categorical column indicating the level of education of the employee. Employee Count: An integer column indicating the number of employees in the company. Environment Satisfaction: An integer column indicating the satisfaction level of the employee with the work environment. Hourly Rate: An integer column indicating the hourly rate of pay for the employee. Job Involvement: An integer column indicating the level of involvement of the employee in their job. Job Level: An integer column indicating the level of the employee's job. Job Satisfaction: An integer column indicating the satisfaction level of the employee with their job. Monthly Income: An integer column indicating the monthly income of the employee. Monthly Rate: An integer column indicating the monthly rate of pay for the employee. Num Companies Worked: An integer column indicating the number of companies the employee has worked for in the past. Percent Salary Hike: An integer column indicating the percentage increase in salary for the employee. Performance Rating: An integer column indicating the performance rating of the employee. Relationship Satisfaction: An integer column indicating the satisfaction level of the employee with their work relationships. Standard Hours: An integer column indicating the standard number of hours worked by the employee. Stock Option Level: An integer column indicating the level of stock options held by the employee. Total Working Years: An integer column indicating the total number of years the employee has worked. Work Life Balance: An integer column indicating the work-life balance of the employee. Years At Company: An integer column indicating the number of years the employee has worked in the company. Years In Current Role: An integer column indicating the number of years the employee has been in their current role. Years Since Last Promotion: An integer column indicating the number of years since the employee's last promotion. Years With Curr Manager: An integer column indicating the number of years the employee has worked with their current manager.

In 2019, ** percent of large sized organizations revealed that they possess a licensed/ on premise Human Resources Management System(HRMS). During the survey, organizations headquartered in Europe, the Middle East and Africa reported that they utilize on average **** different communication methods within the organization.

This database contains only a very small subset of the Human Resources Operational Data Store data. It supports the SSA Employee and Office Data Retrieval (SEODR) Core Services function. This service is used by management personnel to obtain job-related information about an SSA employee.

Personnel data held within the HR database containing personal data relating to Met Office staff

Human Resources datasets related to individual staff eg learning and development.

Contains SSA Employee Information.

Employee Performance & HR Analytics Dataset (India) This dataset contains 5000 employee records with key performance indicators (KPIs), attendance, task completion rates, and manager feedback. It is designed for HR analytics, performance evaluation, and workforce optimization.

📊 Use Cases: ✔ HR decision-making & performance tracking ✔ Employee KPI trend analysis ✔ Workforce productivity assessment ✔ Machine learning model training for promotion prediction

📌 Key Features:

5000 employee records from different departments (IT, HR, Sales, Marketing, Finance). 12 performance-related metrics, including KPI scores, peer ratings, and attendance. Realistic Indian names & job roles generated for authenticity. Ideal for HR analytics dashboards using R (Shiny, ggplot2, DT). 💡 Best for: HR professionals, data analysts, and researchers in workforce performance analysis.

Periodic data extract from Civil Service HR management system (HRMS) which is used to derive staffing reports for Local HR Units and Central HR Policy Division in DPER

Graph and download economic data for Employed full time: Wage and salary workers: Human resources workers occupations: 16 years and over (LEU0257855600A) from 2011 to 2024 about human resources, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

The Green Human Resource Management research & publication dataset, which was indexed by Scopus from 2008 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

The Department of Education contributes to the maintenance of effective human resource management including recruitment, payroll, industrial relations, workplace health and safety, employment equity and diversity, workforce planning and reporting.

*This dataset is no longer being updated. For more information please refer to Workforce statistics at https://www.forgov.qld.gov.au/human-resources/workforce-planning/workforce-statistics-and-tools/workforce-statistics