🟡Please Upvote my dataset If you like It.✨

This dataset contains valuable employee information over time that can be analyzed to help optimize key HR functions. Some potential use cases include:

Attrition analysis: Identify factors correlated with attrition like department, role, salary, etc. Segment high-risk employees. Predict future attrition.

Performance management: Analyze the relationship between metrics like ratings, and salary increments. recommend performance improvement programs.

Workforce planning: Forecast staffing needs based on historical hiring/turnover trends. Determine optimal recruitment strategies.

Compensation analysis: Benchmark salaries vs performance, and experience. Identify pay inequities. Inform compensation policies.

Diversity monitoring: Assess diversity metrics like gender ratio over roles, and departments. Identify underrepresented groups.

Succession planning: Identify high-potential candidates and critical roles. Predict internal promotions/replacements in advance.

Given its longitudinal employee data and multiple variables, this dataset provides rich opportunities for exploration, predictive modeling, and actionable insights. With a large sample size, it can uncover subtle patterns. Cleaning, joining with other contextual data sources can yield even deeper insights. This makes it a valuable starting point for many organizational studies and evidence-based decision-making.

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This dataset contains information about different attributes of employees from a company. It includes 1000 employee records and 12 feature columns.

The columns are:

satisfaction_level: Employee satisfaction score (1-5 scale) last_evaluation: Score on last evaluation (1-5 scale) number_project: Number of projects employee worked on average_monthly_hours: Average hours worked in a month time_spend_company: Number of years spent with the company work_accident: If an employee had a workplace accident (yes/no) left: If an employee has left the company (yes/no) promotion_last_5years: Number of promotions in last 5 years Department: Department of the employee Salary: Annual salary of employee satisfaction_level: Employee satisfaction level (1-5 scale) last_evaluation: Score on last evaluation (1-5 scale)

Attrition of nurses in the US Healthcare system is at an all-time high. It is a major area of focus, especially for hospitals.

This dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. Attrition - whether an employee left or not - is included and can be used as the target variable.

The data is synthetic and based on the IBM Watson dataset for attrition. Employee roles and departments were changed to reflect the healthcare domain. Also, known outcomes for some employees were changed to help increase the performance of ML models.

Here's an app I use as a demo based on this dataset and an ML classification model.

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Project: Human Recourses Analysis - Human_Resources.csv

Description:

The dataset, named "Human_Resources.csv", is a comprehensive collection of employee records from a fictional company. Each row represents an individual employee, and the columns represent various features associated with that employee.

The dataset is rich, highlighting features like 'Age', 'MonthlyIncome', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department', 'EducationField', 'JobSatisfaction', and many more. The main focus is the 'Attrition' variable, which indicates whether an employee left the company or not.

Employee data were sourced from various departments, encompassing a diverse array of job roles and levels. Each employee's record provides an in-depth look into their background, job specifics, and satisfaction levels.

The dataset further includes specific indicators and parameters that were considered during employee performance assessments, offering a granular look into the complexities of each employee's experience.

For privacy reasons, certain personal details and specific identifiers have been anonymized or fictionalized. Instead of names or direct identifiers, each entry is associated with a unique 'EmployeeNumber', ensuring data privacy while retaining data integrity.

The employee records were subjected to rigorous examination, encompassing both manual assessments and automated checks. The end result of this examination, specifically whether an employee left the company or not, is clearly indicated for each record.

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

The 2018 APS employee census was administered to all available Australian Public Service (APS) employees. This census approach provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The census' content is designed to establish the views of APS employees on workplace issues such as leadership, learning and development, and job satisfaction. The census ran from 7 May to 8 June 2018. Overall, 103,137 APS employees responded to the employee census, a response rate of 74%.

Please be aware that the very large number of respondents to the employee census means these files are over 200 mb in size. Downloading and opening these files may take some time.

TECHNICAL NOTES

Three files are available for download.

• 2018 APS employee census - Questionnaire: This contains the 2018 APS employee census questionnaire.

• 2018 APS employee census - 5 point dataset.csv: This file contains individual responses to the 2018 APS employee census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.

• 2018 APS employee census - 5 point dataset.sav: This file contains individual responses to the 2018 APS employee census for use with the SPSS software package.

To protect the privacy and confidentiality of respondents to the 2018 APS employee census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.

A recommended short citation is: 2018 APS employee census data, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

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.

The 2020 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 12 October to 13 November 2020. This was delayed from the usual May to June timeframe due to the impact of COVID-19.

The Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The Census' content is designed to establish the views of APS employees on workplace issues such as leadership, learning and development, and job satisfaction.

Overall, 108,085 APS employees responded to the Employee Census in 2020, a response rate of 78%.

Please be aware that the very large number of respondents to the employee census means these files are over 200MB in size. Downloading and opening these files may take some time.

TECHNICAL NOTES

Three files are available for download.

• 2020 APS Employee Census - Questionnaire: This contains the 2020 APS Employee Census questionnaire.

• 2020 APS Employee Census - 5 point dataset.csv: This file contains individual responses to the 2020 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.

• 2020 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2020 APS Employee Census for use with the SPSS software package.

To protect the privacy and confidentiality of respondents to the 2020 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. A recommended short citation is: 2020 APS Employee Census data, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,610 samples, to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High) Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

Dataset

This dataset was created by JAYESH CHAUHAN

Released under ODC Public Domain Dedication and Licence (PDDL)

Contents

The San Francisco Controller's Office maintains a database of the salary and benefits paid to City employees since fiscal year 2013. This data is summarized and presented on the Employee Compensation report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on a bi-annual basis when available for each fiscal and calendar year.

The 2021 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 10 May to 11 June 2021.

The Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The Census' content is designed to establish the views of APS employees on workplace issues such as leadership, learning and development, and job satisfaction.

Overall, 109,537 APS employees responded to the Employee Census in 2021, a response rate of 77%.

Please be aware that the very large number of respondents to the employee census means these files are over 200MB in size. Downloading and opening these files may take some time.

TECHNICAL NOTES

Three files are available for download.

• 2021 APS Employee Census - Questionnaire: This contains the 2021 APS Employee Census questionnaire.
• 2021 APS Employee Census - 5 point dataset.csv: This file contains individual responses to the 2021 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.
• 2021 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2021 APS Employee Census for use with the SPSS software package.

To protect the privacy and confidentiality of respondents to the 2021 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.

A recommended short citation is: 2021 APS Employee Census data, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

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A. SUMMARY The San Francisco Controller's Office maintains a database of the salary and benefits paid to City employees since fiscal year 2013.

B. HOW THE DATASET IS CREATED This data is summarized and presented on the Employee Compensation report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format.

C. UPDATE PROCESS New data is added on a bi-annual basis when available for each fiscal and calendar year.

D. HOW TO USE THIS DATASET Before using please first review the following two resources: Data Dictionary - Can be found in 'About this dataset' section after click 'Show More' Employee Compensation FAQ - https://support.datasf.org/help/employee-compensation-faq

The 2016 Australian Public Service (APS) employee census was administered to all available APS employees. This census approach provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The census' content is designed to establish the views of APS employees on workplace issues such as leadership, learning and development and job satisfaction. The census ran from 9 May to 10 June 2016. Overall, 96,672 APS employees responded to the employee census, a response rate of 69%.

Please be aware that the very large number of respondents to the employee census means these files are over 200 mb in size. Downloading and opening these files may take some time.

TECHNICAL NOTES

Three files are available for download.

• 2016 APS employee census - Questionnaire: This contains the 2016 APS employee census questionnaire.

• 2016 APS employee census - 5 point dataset.csv: This file contains individual responses to the 2016 APS employee census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.

• 2016 APS employee census - 5 point dataset.sav: This file contains individual responses to the 2016 APS employee census for use with the SPSS software package.

To protect the privacy and confidentiality of respondents to the 2016 APS employee census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.

A recommended short citation is: 2016 APS employee census data, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

The 2013 APS employee census was administered to all available Australian Public Service (APS) employees. This census approach provides a comprehensive view of the APS and ensures no eligible …Show full descriptionThe 2013 APS employee census was administered to all available Australian Public Service (APS) employees. This census approach provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The census ran from 15 May to 14 June 2013. Overall, 102,219 employees responded to the employee census, a response rate of 66%. Please be aware that the very large number of respondents to the employee census means these files are up to 200 mb in size. Downloading and opening these files may take some time. TECHNICAL NOTES Three files are available for download. 2013 APS employee census - Questionnaire: This contains the 2013 APS employee census questionnaire. 2013 APS employee census - 5 point dataset.csv: This file contains individual responses to the 2013 employee census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. 2013 APS employee census - 5 point dataset.sav: This file contains individual responses to the 2013 employee census for use with the SPSS software package. To protect the privacy and confidentiality of respondents to the 2013 APS employee census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions. Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. A recommended short citation is: 2013 APS employee census data, Australian Public Service Commission. Any queries can be directed to research@apsc.gov.au.

Summary

The 2024 APS Employee Census was administered to eligible Australian Public Service (APS) employees between 6 May and 7 June 2024. Overall, 140,396 APS employees responded to the APS Employee Census in 2024, a response rate of 81%.

The APS Employee Census is an annual employee perception survey of the Australian Public Service workforce. The APS Employee Census has been conducted since 2012 and collects APS employee opinions and perspectives on a range of topics, including employee engagement, wellbeing, and leadership.

The APS Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error.

Please be aware that the very large number of respondents to the APS Employee Census means these files are over 200MB in size.

Downloading and opening these files may take some time.

Technical notes

Three files are available for download.

• 2024 APS Employee Census - Questionnaire: This contains the 2024 APS Employee Census questionnaire.

• 2024 APS Employee Census - 5 point dataset with data values: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data values.

• 2024 APS Employee Census - 5 point dataset with data labels: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data labels.

• 2024 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2024 APS Employee Census for use with the SPSS software package.

• 2024 APS Employee Census - data dictionary: This file contains a list of variables and labels within the APS Employee Census.

To protect the privacy and confidentiality of respondents to the 2024 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.

A recommended short citation is: 2024 APS Employee Census, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

The 2022 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 9 May to 10 June 2022. The Employee Census provides a comprehensive view of the …Show full descriptionThe 2022 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 9 May to 10 June 2022. The Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The Census' content is designed to establish the views of APS employees on workplace issues such as leadership, employee wellbeing, and job satisfaction. Overall, 120,662 APS employees responded to the Employee Census in 2022, a response rate of 83%. Please be aware that the very large number of respondents to the employee census means these files are over 200MB in size. Downloading and opening these files may take some time. TECHNICAL NOTES Three files are available for download. 2022 APS Employee Census - Questionnaire: This contains the 2022 APS Employee Census questionnaire. 2022 APS Employee Census - 5 point dataset.csv: This file contains individual responses to the 2022 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. 2022 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2022 APS Employee Census for use with the SPSS software package. To protect the privacy and confidentiality of respondents to the 2022 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions. Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. A recommended short citation is: 2022 APS Employee Census data, Australian Public Service Commission. Any queries can be directed to research@apsc.gov.au.

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