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.

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.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Sample data for exercises in Further Adventures in Data Cleaning.

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

The national Survey of Information Technology Occupations, conducted in 2002 on behalf of the Software Human Resource Council (SHRC), is the first to shed light on the IT labour market in both the public and private sectors. IT employers and employees were surveyed separately, but simultaneously. The employer survey consisted of questions on occupation profile, hiring and recruitment, employee retention, and training and development. The employee survey had questions on the occupational history of IT employees, salary, education, training, and skills. The target population consisted of private sector locations with at least six employees, and with at least one employee working in IT, as well as public-sector divisions with at least one IT employee. The NSITO is a three-stage survey. First, a sample of employers in both private and public sectors is selected; this is stage 1. The questions asked in stage 1 are essentially about the IT workforce. Stage 2 involves selecting a maximum of two occupations (out of 25) per employer. The questions asked in this stage deal with hiring, training and retaining employees in the selected occupations. In stage 3, a maximum of 10 employees are sampled for each occupation selected in stage 2. Among the subjects that employees are asked about are training, previous employment and demographic characteristics. For National Survey of Information Technology Occupations data, refer to Statistics Canada.

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.

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. ​

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...

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 dataset originates from a special mental health survey conducted by a construction company targeting employees assigned to work at an oilfield in a Middle Eastern region. The dataset mainly consists of two documents: raw scale score data and a mental health assessment questionnaire. The following sections describe the data generation process, content structure, spatiotemporal information, and data characteristics:1. Data Generation Process and Processing Methods• Survey Design: A cross-sectional research design was adopted. An online questionnaire was distributed to 400 on-site employees in the Middle Eastern region (April–May 2024). A total of 326 valid responses were collected (valid response rate: 81.5%).• Assessment Tools: Eight standardized scales were used to comprehensively evaluate mental health status, including:◦ General Health Questionnaire (GHQ-12)◦ Patient Health Questionnaire (PHQ-9, depression screening)◦ Generalized Anxiety Disorder Scale (GAD-7)◦ Maslach Burnout Inventory – General Survey (MBI-GS-15)◦ Core Occupational Stress Scale (COSS-17)◦ Social Support Survey (8 items)◦ Iraq-Specific Social Environmental Stress Questionnaire (10 items)◦ Pittsburgh Sleep Quality Index (PSQI-9)2. Spatiotemporal Information• Time Range: Data were collected from April to May 2024, reflecting employees’ psychological state over the past 2–3 weeks.• Spatial Range: Respondents were all located at on-site workplaces in a Middle Eastern region, classified as a high-risk and unstable area.• Resolution: Individual-level data; no specific spatiotemporal resolution required.3. Data Content and Structure(1) Raw Scale Score Data (326 records)• Row Labels: Each record represents one employee’s complete assessment result (326 rows in total).• Column Labels:◦ Basic Information (14 columns): ID, age, gender, education, marital status, number of children, etc.◦ Work Characteristics (8 columns): Job type (management/frontline), years of service, shift cycle, day–night rotation, consecutive days on duty, weekly overtime hours, etc.◦ Lifestyle (4 columns): Number of cohabitants, smoking, drinking, exercise habits.◦ Scale Scores (80 columns): Raw scores for 8 scales, classification results (e.g., “depression detected”), and sub-dimension scores (e.g., emotional exhaustion under burnout).• Units of Measurement:◦ Continuous variables: Age (years), years of service (years), days on duty (days), overtime hours (hours/week).◦ Categorical variables: Gender (male/female), education (junior high school to bachelor’s degree), marital status, etc., encoded as categorical values (e.g., “1 = married”).◦ Scale scores: Scored according to standard rules for each scale (e.g., PHQ-9 total score 0–27).(2) Assessment Questionnaire• Includes an 87-item structured questionnaire covering all items and options of the above 8 scales.4. Data Missingness and Error Notes• Missing Data:◦ Some fields in EXCEL contain blanks (marked as “(blank)”), such as open-ended questions left unanswered (e.g., reasons for sleep disturbances).◦ Missing Data Handling: Valid case analysis was applied; no imputation methods were used.• Sources of Error:◦ Subjective bias in self-report scales (e.g., social desirability effect).◦ Sampling bias: Male participants accounted for 99.7%; female sample was very limited (n=1).◦ Cultural differences: The validity of some scales (e.g., social support) may be limited in cross-cultural contexts.5. File Format and Usage Recommendations• File Format:◦ DOC.◦ Standard .xlsx format (compatible with Excel, WPS, etc.).• Applicable Software:◦ Statistical Analysis: SPSS, R, Python (pandas library).SummaryThis dataset systematically assessed the mental health status and influencing factors of construction employees assigned to high-risk areas in Iraq using standardized scales. It includes both raw data and survey instruments, making it suitable for research in occupational health psychology and cross-cultural adaptation. Caution is advised regarding the gender imbalance in the sample and the potential influence of cultural context on scale validity.

BackgroundThe COVID-19 pandemic has accelerated the transition to remote work, leading to increased attention on presenteeism and absenteeism among remote workers. Understanding the implications of these phenomena on worker health and productivity is crucial for optimizing remote work arrangements and developing policies to improve employee well-being.ObjectivesThis scoping review aims to examine the occurrence of presenteeism and absenteeism among remote workers during the COVID-19 pandemic and the interrelated physical and mental health issues during these periods.MethodsPsycINFO, Medline, Embase, CINAHL, Eric, Business Source Premier, SCOPUS, and sociological abstracts were searched resulting in 1792 articles. Articles were included if the population of interest was 18+ (i.e., working age), engaged in full or part-time work, and the employees shifted from in-person to remote work due to the COVID-19 pandemic. All study designs, geographical areas, and papers written post-onset of the COVID-19 pandemic were included; however, systematic reviews were excluded. Data was charted into Microsoft Excel by 2 independent reviewers.ResultsThe literature search identified 10 studies (i.e., seven cross-sectional studies, two qualitative studies, and one observational study). Five major overarching themes were identified specifically (1) telework and mental health (2) telework and physical health (3) worker benefits (4) gender dynamics and (5) difficulty navigating the teleworking environment. While remote work offers flexibility in terms of saved commute time and flexible work schedules, it also exacerbates challenges related to presenteeism, absenteeism, and work-life balance. These challenges include experiencing psychological distress, depression, anxiety, stress, sleep deprivation, musculoskeletal pain, difficulties concentrating at work for both women and working parents, struggles disconnecting after hours, and the inability to delineate between the work and home environment.DiscussionThe findings suggest that remote work during the COVID-19 pandemic has both positive and negative implications for worker well-being and productivity. However, future research needs to incorporate the potential effects of telework frequency (full time vs. part time) on employee productivity and its role on presenteeism and absenteeism, to gain a more comprehensive understanding on remote work difficulties. Addressing these challenges requires proactive interventions and support mechanisms to promote worker health and productivity in remote settings.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Success.ai’s B2B Email Data for European Marketing & Advertising Professionals provides deep access to the people driving customer growth, brand strategy, and media campaigns across Europe. Built from our 700M+ professional profile dataset, this segment focuses specifically on marketers, advertising managers, digital strategists, and CMOs across all EU and non-EU markets. You’ll receive verified work emails, phone numbers, company info, and LinkedIn profiles for every contact.

Why Use Success.ai for European Marketing Data? Country-Targeted Accuracy: Drill down by country (e.g., Germany, UK, France, Netherlands). Role-Specific Access: Target CMOs, digital leads, performance marketers, and agency execs. Firmographic Precision: Filter by industry, company size, and employee range. GDPR-Safe & Fully Verified: Ethically sourced and validated to 99% accuracy. Seamless Delivery: Receive via API, CSV, or Excel. Ready for import.

Data Fields Available: Work Email Address (verified) Phone Number (optional) Company Name & Domain Job Title & Function LinkedIn URL Industry, Region, Country

Popular Applications: B2B Email Campaigns: Reach digital leads in top European markets. Demand Gen & Funnel Building: Feed your CRM with conversion-ready contacts. Agency Outreach: Connect with media buyers, campaign directors, and brand strategists. Event Marketing: Invite marketing leaders to webinars, meetups, and product launches. ABM Targeting: Build account lists tailored to specific verticals and territories.

Perfect for: SaaS & Martech vendors Advertising platforms Agencies & media buyers Events & conference organizers Design & brand services

Why Success.ai? - Best Price Guarantee: You’ll never overpay for quality data again. - Local + Regional Precision: From London to Stockholm to Milan—we’ve got the coverage. - Real-Time Updating: Stay current with dynamic changes in employment and company data. - Dedicated Manager Support: Get custom queries fulfilled on demand.

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).

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.

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