####About Dataset

This dataset was retrieved from the page https://ai-jobs.net/salaries/download/

This site collects salary information anonymously from professionals all over the world in the AI, ML, Data Science space and makes it publicly available for anyone to use, share and play around with.

The primary goal is to have data that can provide better guidance in regards to what's being paid globally. So newbies, experienced pros, hiring managers, recruiters and also startup founders or people wanting to make a career switch can make better informed decisions.

work_year: The year the salary was paid. experience_level: The experience level in the job during the year with the following possible values: EN: Entry-level / Junior MI: Mid-level / Intermediate SE: Senior-level / Expert EX: Executive-level / Director employment_type: The type of employement for the role: PT: Part-time FT: Full-time CT: Contract FL: Freelance job_title: The role worked in during the year. salary: The total gross salary amount paid. salary_currency: The currency of the salary paid as an ISO 4217 currency code. salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year via data from fxdata.foorilla.com). employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code. remote_ratio: The overall amount of work done remotely, possible values are as follows: 0: No remote work (less than 20%) 50: Partially remote/hybrid 100: Fully remote (more than 80%) company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code. company_size: The average number of people that worked for the company during the year: S: less than 50 employees (small) M: 50 to 250 employees (medium) L: more than 250 employees (large)

1) Data Introduction • The Corporate_work_hours_productivity Dataset is sample data that includes working hours, productivity indicators, demographics, etc. collected from various departments and jobs to analyze the relationship between working hours and productivity of employees in the enterprise.

2) Data Utilization (1) Corporate_work_hours_productivity Dataset has characteristics that: • his dataset consists of various variables such as employee working hours, department, position, productivity score, project participation history, demographics (gender, age, etc.), allowing you to correlate work patterns and productivity and analyze departmental/job characteristics. (2) Corporate_work_hours_productivity Dataset can be used to: • Working Hours and Productivity Analysis: Using employee-specific working hours and productivity indicators, it can be used to analyze the impact of working hours changes on productivity and to derive optimal working hours strategies. • Comparison of work efficiency by department and job: By analyzing productivity differences by department, job, and demographics, it can be used to improve efficiency within an organization, establish personnel strategies, and develop customized work policies.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

Dataset

This dataset was created by Nitin Digraje

Contents

employees according to work center and gender (HQ, Amman CC, Azarqa free zone) Data and Resources اعداد الموظفين حسب مركز العملXLS اعداد الموظفين حسب مركز العمل Explore Preview Download اعداد الموظفين حسب مركز العملCSV اعداد الموظفين حسب مركز العمل Explore Preview Download

The Federal Railroad Administration (FRA) sponsored a study of the work schedules and sleep patterns of railroad employees. The purpose of the study was to understand work-schedule related fatigue that affects various categories of railroad employees by documenting a group's work/rest schedules and sleep patterns to ascertain their impact on the level of fatigue/alertness.Employees surveyed include: signalmen, maintenance of way (MOW) workers, dispatchers, and train & engine service workers (in both freight and passenger train service)

The data was obtained through a questionnaire survey. We distributed measurement questionnaires to 300 full-time employees in China and collected 258 valid questionnaires. For the collected data, we use Excel to input and analyze it in SPSS software. The data is all personal data, and the individuals providing the data are all Chinese citizens. The survey was conducted in the southeastern region of China from January 2023 to April 2023. The data consists of 258 rows, each representing the survey test results of one respondent. The data consists of 33 columns, with the first column representing the sample number, and each subsequent column representing the results of each question for the control and measurement variables.

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)

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

Spain Job Offers Scraped Data

Uncovering Qualifications and Requirements

By [source]

About this dataset

This dataset contains valuable web scraping information about job offers located in Spain, and gives details such as the offer name, company, location, and time of offer to potential employers. Having this knowledge is incredibly beneficial for any job seeker looking to target potential employers in Spain, understand the qualifications and requirements needed to be considered for a role and know approximately how long an offer is likely to stay on Linkedin. This dataset can also be extremely useful for recruiters who need a detailed overview of all job offers currently active in the Spanish market in order to filter out relevant vacancies. Lastly, professionals who have an eye on the Spanish job market can especially benefit from this dataset as it provides useful insights that can help optimise their search even more. This dataset consequently makes it easy for users interested in uncovering opportunities within Spain’s labour landscape with access detailed information about current job opportunities at their fingertips

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How to use the dataset

This guide will help those looking to use this dataset to discover the job market in Spain. The data provided in the dataset can be a great starting point for people who want to optimize their job search and uncover potential opportunities available.

• Understand What Is Being Measured:The dataset contains details such as a job offer name, company, and location along with other factors such as time of offer and type of schedule asked. It is important to understand what each column represents before using the data set.
• Number of Job Offers Available:This dataset provides an insight on how many job offers are available throughout Spain by showing which areas have a high number of jobs listed and what types of jobs are needed in certain areas or businesses. This information could be used for expanding your career or for searching for specific jobs within different regions in Spain that match your skillset or desired salary range .
• Required Qualifications & Skill Set:The type of schedule being asked by businesses is also mentioned, allowing users to understand if certain employers require multiple shifts, weekend work or hours outside the normal 9 - 5 depending on positions needed within companies located throughout the country . Additionally, understanding what skills sets are required not only quality you prioritize when learning new technologies or gaining qualifications but can give you an idea about what other soft skills may be required by businesses like team work , communication etc..
• Location Opportunities:This web scraping list allows users to gain access into potential companies located throughout Spain such as Madrid , Barcelona , Valencia etc.. By understanding where business demand exists across different regions one could look at taking up new roles with higher remuneration , specialize more closely in recruitments/searches tailored specifically towards various regions around Spain .

By following this guide, you should now have a robust understanding about how best utilize this dataset obtained from UOC along with an increased knowledge on identifying job opportunities available through webscraping for those seeking work experience/positions across multiple regions within the country

Research Ideas

• Analyzing the job market in Spain - Companies offering jobs can be compared and contrasted using this dataset, such as locations of where they are looking to hire, types of schedules they offer, length of job postings, etc. This information can let users to target potential employers instead of wasting time randomly applying for jobs online.
• Optimizing a Job Search- Web scraping allows users to quickly gather job postings from all sources on a daily basis and view relevant qualifications and requirements needed for each post in order to better optimize their job search process.
• Leveraging data insights – Insights collected by analyzing this web scraping dataset can be used for strategic advantage when creating LinkedIn or recruitment campaigns targeting Spanish markets based on the available applicants’ preferences – such as hours per week or area/position within particular companies typically offered in the datas set available from UOC

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

L...

Experimental estimates from the Annual Population Survey for homeworking in the UK, including breakdowns by sex, full-time or part-time, ethnicity, occupation, industry, qualifications, hours worked, pay and sickness absence among others. Includes regression outputs on the different outcomes for homeworkers.

History of Work

Here you find the History of Work resources as Linked Open Data. It enables you to look ups for HISCO and HISCAM scores for an incredible amount of occupational titles in numerous languages.

Data can be queried (obtained) via the SPARQL endpoint or via the example queries. If the Linked Open Data format is new to you, you might enjoy these data stories on History of Work as Linked Open Data and this user question on Is there a list of female occupations?.

NEW version - CHANGE notes

This version is dated Apr 2025 and is not backwards compatible with the previous version (Feb 2021). The major changes are: - incredible simplification of graph representation (from 81 to 12); - use of sdo (https://schema.org/) rather than schema (http://schema.org); - replacement of prov:wasDerivedFrom with sdo:isPartOf to link occupational titles to originating datasets; - etl files (used for conversion to Linked Data) now publicly available via https://github.com/rlzijdeman/rdf-hisco; - update of issues with language tags; - specfication of language tags for english (eg. @en-gb, instead of @en); - new preferred API: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/sparql (old API will be deprecated at some point: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/services/historyOfWork-all-latest/sparql ) .

There are bound to be some issues. Please leave report them here.

Figure 1. Part of model illustrating the basic relation between occupations, schema.org and HISCO. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca5521" alt="hisco-basic">

Figure 2. Part of model illustrating the relation between occupation, provenance and HISCO auxiliary variables. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca551e" alt="hisco-aux">

IMPORTANT: This dataset is an historic series that will no longer be updated. This series is now maintained by Employment, Social Security and Housing, from quarter 4 2024 onwards. For the most current data please see: https://opendata.gov.je/dataset/back-to-work Data on numbers of people registered as actively seeking work (ASW) in Jersey. It is important to note that unemployed Jersey residents are not required to register as ASW. There are however certain requirements for those in receipt of an income support claim. Changes to the income support criteria, as well as administrative decisions within Employment, Social Security and Housing, can have an impact on the total numbers registered as ASW. On a more historical basis, the introduction of Income Support in 2008 led to the inclusion of a greater number of individuals in the registered figures. The numbers shown therefore constitute an informative set of indicators demonstrating the level of individuals registered as actively seeking work in the Island at a given point in time. The latest reports on registered actively seeking work are available here.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/DuAgWXDTWUTMy5dF9g8qg People living in households with very low work intensity are people aged 0-59 living in households where the adults work 20% or less of their total work potential during the past year.

Contains the annual summation of employee hours and coal production reported by mine operators where the average quarterly employment is greater than zero with grouping by calendar year, subunit code and mine ID. The subunit code identifies the location or operation of the mine relating to the: (01) Underground; (02) Surface at underground; (03) Strip, quarry, open pit; (04) Auger; (05) Culm bank/refuse pile; (06) Dredge; (12) Other mining; (17) Independent shops or yards; (30) Mill operation/preparation plant; (99) Office workers at mine site.

Statistics on the number of people who obtained Jordanian citizenship or renounced it for the year 2022

Content

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated weekly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Quino Al on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Analysis of ‘HR Analytics: Job Change of Data Scientists’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context and Content

A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.

This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.

The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target

Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.

# Features #
- enrollee_id : Unique ID for candidate

• city: City code

• city_ development _index : Developement index of the city (scaled)

• gender: Gender of candidate

• relevent_experience: Relevant experience of candidate

• enrolled_university: Type of University course enrolled if any

• education_level: Education level of candidate

• major_discipline :Education major discipline of candidate

• experience: Candidate total experience in years

• company_size: No of employees in current employer's company

• company_type : Type of current employer

• last_new_job: Difference in years between previous job and current job

• training_hours: training hours completed

• target: 0 – Not looking for job change, 1 – Looking for a job change

Inspiration

• Predict the probability of a candidate will work for the company
• Interpret model(s) such a way that illustrate which features affect candidate decision # Please refer to the following task for more details: https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015

--- Original source retains full ownership of the source dataset ---

This dataset shows the Employment in The ICT Industry, 2005 - 2021 value below are estimate Base year Year 2005 2012 2010 2016 value below are preliminary Base year Year 2005 2013 2010 2017 No. of Views : 52

This dataset contains annual average CES data for California statewide and areas from 1990 to 2023.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.