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

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2020. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

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

This project focuses on HR analysis of employee attrition and factors related to it. In this dashboard, I have included KPI's like total employee count, Attrition Count, Attrition Rate, and average age.

Findings: There are a total of 1470 employees in the firm. 237 number of employees have left the company. Rate of Attrition: 16.1%, which is low. Average Age: 37 years old.

99 of the 273 employees who left had only a bachelor's degree, and 50–60% were sales executives or laboratory technicians. When we look at attrition by department, the majority of the employees were from the human resources department.

This dashboard is an example of how data visualization can help the HR department make informed decisions. It's user-friendly, interactive, and provides a variety of charts and graphs that help users gain a deeper understanding of the data.

One of the most impressive features of this dashboard is its ability to filter data. Users can filter the data by education, allowing them to focus on the areas that matter most to them. This feature enables users to gain a better understanding of the data and make informed decisions based on the insights provided.

Download Employee Vehicle Personal Use Excel SheetThis dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2020. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

This is an excel mapping tool that was built based on Caribbean Islands administrative boundaries (admin0). The Map datasets was coming from GADM, Centre National de l'Information Géo-Spatiale (Haiti) and Oficina Nacional de Estadística (Dominican Republic). The population dataset is a sample data. This tool is made for people in the field with limited access to GIS to quickly map their data.

The Employee Satisfaction Survey dataset is a comprehensive collection of information regarding employees within a company. It includes essential details such as employee identification numbers, self-reported satisfaction levels, performance evaluations, project involvement, work hours, tenure with the company, work accidents, promotions received in the last 5 years, departmental affiliations, and salary levels. This dataset offers valuable insights into the factors influencing employee satisfaction and can be used to analyze and understand various aspects of the workplace environment.

A free mapping tool that allows you to create a thematic map of London without any specialist GIS skills or software - all you need is Microsoft Excel. Templates are available for London’s Boroughs and Wards. Full instructions are contained within the spreadsheets.

Borough maps

• Map for quantitative data
• Map for categorical data
• Cartogram that uses squares to represent London Boroughs

Ward maps

Pre-2014 boundaries

• Greater London
• Individual Boroughs: Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, <a href="https://

Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

Why Choose Success.ai’s Ecommerce Store Data?

1. Verified Profiles for Precision Engagement

   • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
   • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.

2. Comprehensive Coverage of the APAC E-commerce Sector

   • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
   • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.

3. Continuously Updated Datasets

   • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
   • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access business profiles for e-commerce professionals and organizations across APAC.
• Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
• Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
• Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

Key Features of the Dataset:

1. Comprehensive E-commerce Business Profiles

   • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
   • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
   • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.

3. Regional and Sector-specific Insights

   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
   • Refine your marketing strategies and outreach efforts to align with market priorities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.

2. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
   • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.

3. Market Research and Competitive Analysis

   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
   • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...

A free mapping tool that allows you to create a thematic map of London without any specialist GIS skills or software - all you need is Microsoft Excel. Templates are available for London’s Boroughs and Wards. Full instructions are contained within the spreadsheets. Macros The tool works in any version of Excel. But the user MUST ENABLE MACROS, for the features to work. There a some restrictions on functionality in the ward maps in Excel 2003 and earlier - full instructions are included in the spreadsheet. To check whether the macros are enabled in Excel 2003 click Tools, Macro, Security and change the setting to Medium. Then you have to re-start Excel for the changes to take effect. When Excel starts up a prompt will ask if you want to enable macros - click yes. In Excel 2007 and later, it should be set by default to the correct setting, but if it has been changed, click on the Windows Office button in the top corner, then Excel options (at the bottom), Trust Centre, Trust Centre Settings, and make sure it is set to 'Disable all macros with notification'. Then when you open the spreadsheet, a prompt labelled 'Options' will appear at the top for you to enable macros. To create your own thematic borough maps in Excel using the ward map tool as a starting point, read these instructions. You will need to be a confident Excel user, and have access to your boundaries as a picture file from elsewhere. The mapping tools created here are all fully open access with no passwords. Copyright notice: If you publish these maps, a copyright notice must be included within the report saying: "Contains Ordnance Survey data © Crown copyright and database rights." NOTE: Excel 2003 users must 'ungroup' the map for it to work.

This is the Department for Work and Pensions (DWP) sixth report under the Public Sector Equality Duty (PSED), part of the Equality Act 2010.

Our equality and diversity page has more information about diversity and equality of opportunity.

We’ve published a separate report on customer data.

Project Priority Matrix (Dynamic Excel Tool)

Overview

This dataset provides a dynamic Excel model for prioritizing projects based on Feasibility, Impact, and Size.
It visualizes project data on a Bubble Chart that updates automatically when new projects are added.

Use this tool to make data-driven prioritization decisions by identifying which projects are most feasible and high-impact.

Goal

Organizations often struggle to compare multiple initiatives objectively.
This matrix helps teams quickly determine which projects to pursue first by visualizing:

• Feasibility → How achievable a project is
• Impact → The potential benefit or value it delivers
• Size → The level of effort or resources required

How It Works

1. Each project is rated on a 1–10 scale for:
   • Feasibility
   • Impact
   • Size
2. The Excel file uses a Bubble Chart:
   • X-axis: Feasibility
   • Y-axis: Impact
   • Bubble size: Project Size
3. The chart automatically updates when new projects or scores are added.

Example (partial data):

Interpretation Guide

Excel Features

• Dynamic Bubble Chart (updates with new data)
• Named Ranges for auto-expanding data
• Optional Conditional Formatting
• Data Validation for consistent scoring

How to Use

1. Download and open Project_Priority_Matrix.xlsx.
2. Go to the Data sheet.
3. Add your project names and scores (1–10).
4. Watch the chart update instantly to reflect your data.

You can use this for: - Portfolio management
- Product or feature prioritization
- Strategy planning workshops

File Information

• File: Project_Priority_Matrix.xlsx
• Format: Excel (.xlsx)
• Version: 1.0
• Last Updated: October 2025

License

Free for personal and organizational use.
Attribution is appreciated if you share or adapt this file.

Author: [Asjad]
Contact: [m.asjad2000@gmail.com]
Compatible With: Microsoft Excel 2019+ / Office 365

Koncile automatically reads and extracts key data from work stoppages: name, reason, dates, duration, doctor, attachments…export via Excel/JSON/API

🛍️ Product Sales Dataset (2023–2024)

📌 Overview

This dataset contains 200,000 synthetic sales records simulating real-world product transactions across different U.S. regions. It is designed for data analysis, business intelligence, and machine learning projects, especially in the areas of sales forecasting, customer segmentation, profitability analysis, and regional trend evaluation.

The dataset provides detailed transactional data including customer names, product categories, pricing, and revenue details, making it highly versatile for both beginners and advanced analysts.

📂 Dataset Structure

• Rows: 200,000
• Columns: 14

Features

1. Order_ID – Unique identifier for each order
2. Order_Date – Date of transaction
3. Customer_Name – Name of the customer
4. City – City of the customer
5. State – State of the customer
6. Region – Region (East, West, South, Centre)
7. Country – Country (United States)
8. Category – Broad product category (e.g., Accessories, Clothing & Apparel)
9. Sub_Category – Subdivision of category (e.g., Sportswear, Bags)
10. Product_Name – Product description
11. Quantity – Units purchased
12. Unit_Price – Price per unit (USD)
13. Revenue – Total sales amount (Quantity × Unit Price)
14. Profit – Net profit earned from the transaction

🎯 Potential Use Cases

• Sales Analysis: Track revenue, profit, and performance by product, category, or region.
• Customer Analytics: Identify top customers, purchasing frequency, and loyalty patterns.
• Profitability Insights: Compare profit margins across categories and sub-categories.
• Time-Series Analysis: Study seasonal demand and forecast future sales.
• Visualization Projects: Build dashboards in Power BI, Tableau, or Excel.
• Machine Learning: Train models for demand prediction, price optimization, or segmentation.

📊 Example Insights

• Which region generates the highest revenue?
• What are the top 10 most profitable products?
• Are some product categories more popular in certain regions?
• Which customers contribute the most to total revenue?

🏷️ Tags

business · sales · profitability · forecasting · customer analysis · retail

📜 License

This dataset is synthetic and created for educational and analytical purposes. You are free to use, modify, and share it under the CC BY 4.0 License.

🙌 Acknowledgments

This dataset was generated to provide a realistic foundation for learning and practicing Data Analytics, Power BI, Tableau, Python, and Excel projects.

The Adventure Works dataset is a comprehensive and widely used sample database provided by Microsoft for educational and testing purposes. It's designed to represent a fictional company, Adventure Works Cycles, which is a global manufacturer of bicycles and related products. The dataset is often used for learning and practicing various data management, analysis, and reporting skills.

Key Features of the Adventure Works Dataset:

1. Company Overview: - Industry: Bicycle manufacturing - Operations: Global presence with various departments such as sales, production, and human resources.

2. Data Structure: - Tables: The dataset includes a variety of tables, typically organized into categories such as: - Sales: Information about sales orders, products, and customer details. - Production: Data on manufacturing processes, inventory, and product specifications. - Human Resources: Employee details, departments, and job roles. - Purchasing: Vendor information and purchase orders.

3. Sample Tables: - Sales.SalesOrderHeader: Contains information about sales orders, including order dates, customer IDs, and total amounts. - Sales.SalesOrderDetail: Details of individual items within each sales order, such as product ID, quantity, and unit price. - Production.Product: Information about the products being manufactured, including product names, categories, and prices. - Production.ProductCategory: Data on product categories, such as bicycles and accessories. - Person.Person: Contains personal information about employees and contacts, including names and addresses. - Purchasing.Vendor: Information on vendors that supply the company with materials.

4. Usage: - Training and Education: It's widely used for teaching SQL, data analysis, and database management. - Testing and Demonstrations: Useful for testing software features and demonstrating data-related functionalities.

5. Tools: - The dataset is often used with Microsoft SQL Server, but it's also compatible with other relational database systems.

The Adventure Works dataset provides a rich and realistic environment for practicing a range of data-related tasks, from querying and reporting to data modeling and analysis.

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

This dataset is a representation of employee performance data, designed to facilitate clustering and classification tasks. It includes key attributes such as age, years of experience, education level, department, and performance score, along with a derived target column categorizing performance into five levels: Poor, Average, Good, Very Good, and Excellent. The dataset is suitable for evaluating machine learning algorithms in tasks like clustering, predictive modeling, and performance evaluation.

Key Features:

age: Age of the employee (20–60 years). years_experience: Total years of professional experience (1–40 years). education_level: Highest educational qualification (High School, Bachelor, Master, PhD). department: Department of work (Sales, Tech, HR, Finance). performance_score: Numerical performance score on a scale of 1–10. performance_category: Target column with categorical performance levels (Poor, Average, Good, Very Good, Excellent).

🏏 IPL Ball-by-Ball Dataset with Pitch & Player Stats (2020–2025)

This dataset includes ball-by-ball delivery data from Indian Premier League (IPL) matches between 2020 and March 2025, enriched with pitch condition information, and match-level batting and bowling performance statistics.

It's ideal for building machine learning models, conducting sports analytics, training fantasy cricket prediction systems, and exploring performance trends in T20 cricket.

📁 Dataset Overview

Files Included:

• Ipl match data - enriched.xlsx – Full ball-by-ball data with pitch types
• all_matches_batting_stats.csv – Player-wise batting stats per match
• all_matches_bowling_stats.csv – Player-wise bowling stats per match

🔍 Key Features

• ⚾ Ball-by-ball records for every delivery across 5 IPL seasons
• 🌱 Pitch condition data: Spin-friendly, Batting-friendly, etc.
• 🧢 Structured performance stats for batting and bowling per match
• 🏟️ Match metadata: Venue, city, date, teams, overs
• ✅ Well-cleaned, analysis-ready Excel and CSV formats

🎯 Use Cases & Keywords

• Fantasy Cricket Prediction Model
• T20 Match Analytics
• IPL Data Visualization Projects
• Cricket Simulation Engine
• Player Form and Consistency Analysis
• Pitch Impact on Performance
• Machine Learning with Cricket Data
• Python/Excel-based IPL Stats Projects

📝 Example Columns

From Ball-by-Ball: - match_id, date, venue, batter, bowler, runs_batter, runs_extras, wicket_taken, dismissal_kind, pitch_type

From Batting Stats: - player, team, runs_scored, balls_faced, fours, sixes

From Bowling Stats: - player, team, overs_bowled, runs_conceded, wickets

📜 License

CC0 1.0 Universal (Public Domain Dedication)
You are free to use this dataset in personal, academic, or commercial projects with no restrictions.