Explore the "CareerBuilder US Jobs Dataset – August 2021," a valuable resource for understanding the dynamics of the American job market.

This dataset features detailed job listings from CareerBuilder, one of the largest employment websites in the United States, and provides a comprehensive snapshot of job postings as of August 2021.

Key Features:

• Extensive Job Listings: Includes thousands of job postings across various industries and sectors, providing a broad view of employment opportunities in the US.
• Detailed Information: Each listing contains essential details such as job titles, company names, locations, job descriptions, employment types (full-time, part-time, contract), required qualifications, and salary ranges.
• Insights into Trends: Analyze trends in employment, including the most in-demand skills, top hiring companies, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research: This dataset is perfect for researchers, HR professionals, and data analysts interested in understanding the current state of the job market, developing recruitment strategies, or studying labor market dynamics.

By leveraging this dataset, you can gain valuable insights into the US job market as of August 2021, helping you stay ahead of industry trends and make informed decisions. Whether you're a job seeker, employer, or researcher, the CareerBuilder US Jobs Dataset offers a wealth of information to explore.

• The dataset is part of Eurostat's collection of population and employment statistics. The code "NAMQ_10_PE" specifically refers to data related to employment and population trends in European countries and likely spans a range of years from 1980 to 2024.

Eurostat provides statistical data on various aspects of the labor market across Europe, including:

• Total Population – The total number of people residing in a particular country or region.
• Labor Force – The portion of the population that is either employed or actively looking for work.
• Employment Rate – The percentage of the working-age population that is employed.
• Unemployment Rate – The percentage of the labor force that is unemployed.
• Youth Employment Rate – The employment rate among young people (typically aged 15-24).

• Sectoral Employment – Employment distribution across various sectors like agriculture, industry, and services.

• **Details of the Dataset **

This dataset would typically cover European Union countries and potentially other European countries (depending on the specific version). The data likely spans multiple years (1980-2024) and provides insights into the demographic and economic changes in these countries over time.

-**Some example insights you might explore:**

Trends in Employment: Analyzing the employment and unemployment rates over time to see how they correlate with major economic events, such as the global financial crisis. Sectoral Shifts: Investigating how the structure of employment has shifted from agriculture and industry to services over the decades. Impact of Population Growth: Exploring how changes in population size relate to changes in employment, labor force participation, and unemployment.

• Link to Eurostat’s Dataset

You can access the Eurostat dataset directly using the following link:

• Eurostat – NAMQ_10_PE Dataset

This link takes you to Eurostat's Labor Force Survey (LFS) data, which includes datasets related to employment, unemployment, and other labor force indicators across EU countries. You can navigate and search for NAMQ_10_PE by using Eurostat’s filtering and search tools. Here, you can download data in various formats such as CSV, Excel, or TSV.

Employment Rate in the United States increased to 59.70 percent in September from 59.60 percent in August of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Number of employees by North American Industry Classification System (NAICS) and type of employee, last 5 years.

Techsalerator’s Job Openings Data in Latin America provides a thorough and insightful dataset designed to deliver businesses, recruiters, labor market analysts, and job seekers with a comprehensive view of employment opportunities across the Latin American region. This dataset aggregates job postings from a diverse array of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Latin America.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates job postings from a variety of sources, including company career sites, job boards, recruitment agencies, and professional networking platforms. This comprehensive coverage ensures that users receive a broad spectrum of job opportunities from multiple channels. Daily Updates:

Data is updated daily, providing real-time insights into job market conditions. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This segmentation allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

Detailed information is provided on job openings across different countries and key regions within Latin America. This regional breakdown helps users understand job market dynamics and opportunities in various geographic areas. Role and Skill Analysis:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in identifying opportunities that match their expertise and helps recruiters find candidates with the desired skill sets. Company Insights:

Users can access information about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking, but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Recruitment: Recruiters and HR professionals can use the data to identify hiring trends, understand competitive practices, and optimize their recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, enhancing the efficiency and effectiveness of their job search. Workforce Planning: Companies can gain valuable insights into the availability of talent across Latin America, assisting with decisions related to market entry, expansion, and talent acquisition. Techsalerator’s Job Openings Data in Latin America is an essential tool for understanding the diverse and evolving job markets across the region. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.

Many people say the gender gap in income levels is overstated in the United States, where some say that inequality in the labor force is a thing of the past. Is there a gender gap at all? Is it stronger in some industries than in others?

This dataset, retrieved from the Bureau of Labor Statistics, shows the median weekly incomes for 535 different occupations. The data encompasses information for all working American citizens as of January 2015. The incomes are broken into male and female statistics, preceded by the total median income when including both genders. The data has been re-formatted from the original PDF-friendly arrangement to make it easier to clean and analyze.

Analysis thus far has found that there is indeed a sizable gender gap between male and female incomes. Use of this dataset should cite the Bureau of Labor Statistics as per their copyright information:

The Bureau of Labor Statistics (BLS) is a Federal government agency and everything that we publish, both in hard copy and electronically, is in the public domain, except for previously copyrighted photographs and illustrations. You are free to use our public domain material without specific permission, although we do ask that you cite the Bureau of Labor Statistics as the source.

Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

Percentage of workforce teleworking or working remotely prior to February 1, 2020, on March 31, 2020, and percentage of workforce able to carry out a majority of their duties during the COVID-19 pandemic, by North American Industry Classification System (NAICS) code, business employment size, type of business and majority ownership.

This dataset summarizes annual employment and wage data collected by the Massachusetts Department of Economic Research (DER) from employers subject to federal and state unemployment compensation laws. This dataset covers the workforce in Cambridge, regardless of their place of residence. Note that the unemployment compensation system does not cover all workers and excludes groups such as the self-employed, religious workers and some domestic workers.

This dataset assigns workers to high-level industries using the Quarterly Census of Employment and Wages (QCEW) super-sectors which are derived from North American Industrial Classification System (NAICS) sectors.

VITAL SIGNS INDICATOR Change in Jobs by Industry (EC2)

FULL MEASURE NAME Employment by place of work by industry sector

LAST UPDATED May 2019

DESCRIPTION Change in jobs by industry is the percent change and absolute difference in the number of people who have jobs within a certain industry type in a given geographical area

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2017 http://www.labormarketinfo.edd.ca.gov/

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).

The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Context:

The Current Employment Statistics (CES) program provides estimates of employment, hours, and earnings information on a national basis and in considerable industry detail. The Bureau of Labor Statistics collects payroll data each month from a sample of business and government establishments in all nonfarm activities.

Employment data include series for total employment, number of women employees, and number of production or nonsupervisory employees. Estimates of average hourly earnings, average weekly hours, average weekly earnings, and average weekly overtime hours are produced for both all employees and for production or nonsupervisory employees. Overtime hours are produced for manufacturing industries only.

A sample of approximately 147,000 businesses and government agencies representing approximately 634,000 worksites throughout the United States is utilized for this monthly survey. The sample contains about 300,000 employer units.

All employment, hours and earnings series are classified according to the 2012 North American Industry Classification System (NAICS). The industry code used in the survey corresponds to the NAICS code, except in those cases where multiple industries have been combined.

Content:

Please refer to ce.txt for a description of how to parse and use the unique identifiers.

This dataset was collected on June 27th, 2017 and may not be up-to-date.

Summary of Data Available: For all employees, women employees, and production or nonsupervisory employees, CES publishes about 4,300 monthly series. The series for all employees cover more than 900 industries on both a seasonally adjusted and not seasonally adjusted basis.

For private-sector industries, nearly 7,500 series are published each month for average weekly earnings, average hourly earnings, average weekly hours, and, in manufacturing, average weekly overtime hours. Hours and earnings data for all employees are available for about 620 industries and for production or nonsupervisory employees about 550 industries.

From the employment, hours, and earnings series, CES produces about 7,500 derivative series, such as indexes and real earnings series.

Most employment series begin in 1990, although some series, including industry supersectors, are available from 1939. Supersectors include: mining and logging; construction; manufacturing; trade, transportation, and utilities; information; financial activities; professional and business services, education and health services; leisure and hospitality, other services, and government.

Frequency of Observations: Data series are monthly in most cases; quarterly averages are available for total employment, average weekly hours, and average overtime hours, seasonally adjusted (datatypes 19, 20, 25, 36, and 37).

Annual averages are available for all series that are not adjusted for seasonality (except for the 12-month diffusion index series).

Data Characteristics: Earnings are measured in dollars and are published to the nearest cent (two decimal places). Average weekly and overtime hours are measured in hours and are published to the nearest tenth of an hour (one decimal place).

Employment is measured in thousands of workers and is stored with no decimal place for all supersectors and for both durable goods and nondurable goods in manufacturing. Employment for all other industries are stored to one decimal place.

Special characteristics of the data are footnoted where necessary. For example; I indicates that the seasonally adjusted series is independently seasonally adjusted and not used in aggregating to higher summary industries. For all employees, higher summary series, such as total nonfarm, are aggregated up from the 3-digit NAICS level.

Each year with the release of January estimates in February, CES data are re-anchored to universe counts of nonfarm employment or benchmarks for the most recent March. For example, CES introduced March 2016 benchmark counts with the release of January 2017 first preliminary estimates in February 2017. On a not seasonally adjusted basis, all series are subject to revision back to the prior year’s benchmarked data (21 mon...

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

VITAL SIGNS INDICATOR Jobs by Industry (EC1)

FULL MEASURE NAME Employment by place of work by industry sector

LAST UPDATED July 2019

DESCRIPTION Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE Bureau of Labor Statistics: Current Employment Statistics 1990-2017 http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).

The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Mental Health in the Tech Industry

Exploring Mental Health Conditions in the Tech Industry

By Olga Tsubiks [source]

About this dataset

This dataset contains survey responses from the tech industry about mental health, offering an insightful snapshot into the diagnoses, treatments, and attitudes of those in the field towards mental health. These data points allow people to understand more about how their peers in tech view mental health and can provide greater insight into how to better support those who work in this industry. This dataset includes questions on whether or not respondents have had a mental health disorder or sought treatment for a mental health issue in the past, if they currently have been diagnosed with a condition and what it is, their age group, location of work and residence as well as information on whether they are self-employed or working at a tech company with other questions. Additionally, this dataset also provides insight into respondents' attitudes towards speaking openly about their mental wellbeing versus physical wellbeing. To gain even more understanding of individual's experiences within their place of business overall employee count is included as well what role they fill within that organisation is related to technology/IT. This valuable data set may be used for medical research furthering our knowledge about workplace stressors effecting people seen within this particular field but also across multiple industries to help create support systems that reflect upon individual need rather than one-size fits all models previously employed by employers through out many parts globally

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Research Ideas

• Analyze the correlation between employment industry and mental health status, including self-identified diagnosis, use of mental health services and any history of mental illness in the family.
• Determine if there are differences in how people experience and speak out about their own mental health based on geographic location.
• Compare attitudes towards open conversations on physical vs mental health within different age groups both in the U.S. and abroad

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: OSMI_Survey_Data.csv | Column name | Description | |:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | Are you selfemployed | Indicates whether the respondent is self-employed or not. (Boolean) | | How many employees does your company or organization have | Indicates the number of employees in the respondent's company or organization. (Numeric) | | Is your employer primarily a tech companyorganization | Indicates whether the respondent's employer is primarily a tech company or organization. (Boolean) | | Is your primary role within your company related to techIT | Indicates whether the respondent's primary role within their company is related to tech or IT. (Boolean) | | Do you have previous employers | Indicates whether the respondent has had previous employers. (Boolean) ...

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels. Disclaimer: For information regarding future updates or preliminary/final data releases, please refer to the Bureau of Labor Statistics Release Calendar: https://www.bls.gov/cew/release-calendar.htm

Key Table Information.Table Title.Means of Transportation to Work by Industry for Workplace Geography.Table ID.ACSDT1Y2024.B08526.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counti...

DO NOT EDIT THIS DATASET.

This dataset, which is automatically updated contains American Community Survey 5-Year Estimates. This dataset is updated by a Socrata process; please contact support@socrata.com if you encounter any questions or issues.

This dataset contains variables from Data Profile 3 (DP03). Topics include: employment status, commuting to work, occupation, industry, class of worker, income and benefits, health insurance coverage, and poverty level, all at the State, County, and Census Tract level for each Tract in the County.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

Labour market status of disabled people, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.