The cities expected by industry experts to have the highest investor demands in the United States in 2023 were chosen due to their sustained population and job growth, attraction to educated millennials, high levels of economic diversity, and white-collar employment among others. Austin, Nashville, and Dallas Fortworth ranked highest among the top 15 cities with the highest projected investor demand in real estate in the United States for 2023.

This dataset provides a comprehensive view of the job market in California, highlighting companies and cities with the highest number of job opportunities. Created by JoPilot, it contains valuable information for anyone interested in the employment landscape across different industries and regions. It includes key information such as:

• Company name • City • State • Number of active jobs

For job seekers, employers, and researchers, this resource can be particularly useful in several ways:

1. Identifying hot job markets: The data highlights cities with the highest number of job openings, helping job seekers focus their search on areas with more opportunities.
2. Company targeting: By showing which companies have the most active job listings, the dataset allows job seekers to target their applications to organizations that are actively hiring.
3. Industry trends: The information can reveal which industries or sectors are experiencing growth in California, guiding career decisions and educational pursuits.
4. Regional comparisons: Users can compare job markets across different California cities and regions, which is valuable for those considering relocation or analyzing economic trends.
5. Skill alignment: While the dataset doesn't directly provide skill requirements, it can be used alongside other resources to align job seekers' skills with in-demand positions.

For a more comprehensive job search strategy, consider complementing this dataset with additional resources such as the California Labor Market Information tools, which offer detailed insights into wages, employment projections, and industry-specific data.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC) and with the membership staff tracks collaborative efforts to recruit business prospects and locates. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe.This dataset provides the target and actual job creation numbers for the City of Tempe and Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population.This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created.Additional InformationSource:Contact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

This statistic shows the cities with the highest high-tech industry job growth rate in the United States between 2006 to 2016. Between 2006 and 2016, the San Francisco-Oakland-Hayward region of California has experienced a 90 percent increase in high-tech industry jobs.

The dataset provided by Tarta.ai, created in February 2023, contains information on the number of jobs by company and city in Massachusetts. The data provides a comprehensive view of the job market, highlighting the companies and cities that have the highest number of job opportunities.

The dataset includes a list of companies and the number of jobs they offer in different cities.

The dataset provides valuable insights for job seekers, employers, and policymakers. It can help job seekers to identify companies and cities with the highest job opportunities in their preferred industry and location. Employers can use the data to understand the competitive landscape and adjust their recruitment strategies accordingly. Policymakers can leverage the information to develop policies that promote job growth and economic development in different regions.

Overall, the Tarta.ai dataset is a valuable resource for anyone interested in the job market and provides a comprehensive view of the employment landscape across different industries and regions.

Dataset Columns:

1. Company name
2. City
3. State
4. Number of active jobs

This statistic shows the ten cities with the highest job growth in the United Kingdom (UK) from 2004 to 2013. The number of jobs located in Milton Keynes increased by 18.2 percent over this nine year period, with London and Cambridge in second and third place respectively.

Analysis of ‘5.02 New Jobs Created (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/73bc502b-2b3a-4ab7-83ad-4649019064d0 on 11 February 2022.

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

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.

The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC) and with the membership staff tracks collaborative efforts to recruit business prospects and locates. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe.

This dataset provides the target and actual job creation numbers for the City of Tempe and Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population.

This page provides data for the New Jobs Created performance measure.

The performance measure dashboard is available at 5.02 New Jobs Created.

Additional Information

Source:
Contact: Jill Buschbacher
Contact E-Mail: Jill_Buschbacher@tempe.gov
Data Source Type: Excel files
Preparation Method: Extracted from GPEC monthly and annual reports and proprietary excel files
Publish Frequency: Annually
Publish Method: manual
Data Dictionary

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

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

This graph shows the best and worst cities for jobs in the United States in spring 2012. Knoxville, Tennessee and Greenville-Mauldin-Easley, South Carolina are both place first with a net employment outlook of 24 percent for the second quarter of 2012.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

Commercial services, the activities operating within the private sector, are attracted to markets according to the population of the area they serve and the level of market income. The growth rates for wholesaling varied regionally, with the higher rates in British Columbia, Alberta, Ontario and Quebec contrasting with those in Saskatchewan, Manitoba and the Atlantic provinces. The highest rates of growth occurred in British Columbia and Alberta, and in or near Toronto, Ottawa and Montréal. These are some of the places with the highest per capita incomes, and generally the places with highest rates of population growth during this period. The highest growth rates are found in small peripheral cities, which had a very small employment base in 1986 (for example, La Baie, Quebec, and Port Alberni and Fort St. John, British Columbia).

Dataset Overview The dataset consists of 26,000 job listings, extracted from a Taiwanese job search platform, focusing on software-related careers. Each listing is detailed with various attributes, providing a comprehensive view of the job market in this sector. Here's a breakdown of the dataset columns:

職缺類別 (Job Category) 職位類別 (Position Category) 職位 (Position) 縣市 (City/County) 地區 (District/Area) 供需人數 (應徵人數) (Number of Applicants) 公司名稱 (Company Name) 職缺名稱 (Job Title) 工作內容 (Job Description) 職務類別 (Job Type) 工作待遇 (Salary) 工作性質 (Nature of Work) 上班地點 (Work Location) 管理責任 (Management Responsibility) 上班時段 (Working Hours) 需求人數 (Number of Positions) 工作經歷 (Work Experience) 學歷要求 (Educational Requirements) 科系要求 (Departmental Requirements) 擅長工具 (Tools Proficiency) 工作技能 (Job Skills) 其他條件 (Other Conditions) 資本額 (Capital Amount) 員工人數 (Number of Employees) 公司標籤 (Company Tags) Analytical Insights Exploratory Data Analysis Perform exploratory data analysis using libraries like Pandas and NumPy. Examine trends in job categories, salaries, and educational requirements. Analyze the distribution of jobs across different cities and districts. Visualization Create visual representations of the dataset using Python visualization libraries. Plot job distribution across various sectors or locations. Visualize salary ranges and compare them with educational and experience requirements. Practice with SQL or Pandas Queries Utilize the dataset to refine SQL query skills or Pandas data manipulation techniques. Execute queries to extract specific information, such as the most in-demand skills or the companies offering the highest salaries. NLP Analysis and Tasks for Software Jobs Dataset This dataset, encompassing 26,000 job listings from the Taiwanese software industry, is ripe for a variety of Natural Language Processing (NLP) analyses. Below are some recommended NLP tasks and analyses that can be conducted on this dataset.

Text Classification Job Category Prediction: Train a classification model to predict the job category (職缺類別) using job descriptions (工作內容). Salary Range Classification: Classify jobs into different salary brackets based on their descriptions and titles, helping to identify features associated with higher salaries. Sentiment Analysis Company Reputation Analysis: Analyze the sentiment of company tags (公司標籤) to assess the general sentiment or reputation of companies listed in the dataset. Topic Modeling Identifying Key Job Requirements: Apply LDA (Latent Dirichlet Allocation) to job descriptions for uncovering common themes or required skills in the software sector. Named Entity Recognition (NER) Information Extraction: Implement NER to extract specific entities like tools (擅長工具), skills (工作技能), and educational qualifications (學歷要求) from job descriptions. Text Summarization Summarizing Job Descriptions: Develop algorithms for generating concise summaries of job descriptions, enabling quick understanding of key points. Language Modeling Job Description Generation: Use language models to create realistic job descriptions based on input prompts, assisting in job listing creation or understanding industry language trends. Machine Translation (If Applicable) Dataset Translation for Global Accessibility: Translate the dataset content into English or other languages for international accessibility, using machine translation models. Predictive Analysis Predicting Applicant Volume: Use historical data to forecast the number of applicants (供需人數 (應徵人數)) a job listing might attract based on various factors. By leveraging these NLP techniques, insightful findings can be extracted from the dataset, beneficial for both job seekers and employers in the software field. This dataset offers a practical opportunity to apply NLP skills in a real-world setting.

License

CC0

Original Data Source: Taiwan 104.com jobs search JD

Techsalerator’s Job Openings Data in Europe is a robust and meticulously curated dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a comprehensive view of employment opportunities across the continent. This dataset aggregates job postings from a wide range of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available in Europe.

Key Features of the Dataset: Comprehensive Coverage:

The dataset captures job postings from numerous sources, including company career pages, job boards, recruitment agencies, and professional networking sites. This broad coverage ensures that the dataset includes job opportunities across various platforms and channels. Daily Aggregation:

Data is updated daily, providing users with real-time insights into the job market. This frequent updating ensures that the information is current and reflects the latest trends and changes in job availability. Sector-Specific Insights:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This categorization helps users analyze job market trends specific to different sectors and industries. Regional Breakdown:

The dataset includes detailed information on job openings across different regions and cities in Europe. This regional breakdown allows users to understand job market dynamics and opportunities in various geographic locations. Role and Skill Analysis:

Data includes information on job roles, required skills, qualifications, and experience levels. This helps job seekers identify opportunities that match their expertise and assists recruiters in finding candidates with the right skill sets. Company Insights:

Users can access data on the companies posting job openings, including company names, industries, and locations. This information is useful for understanding which companies are hiring and where the demand for talent is concentrated. Historical Data:

The dataset may include historical job posting data, enabling trend analysis and comparative studies over time. This feature allows users to track changes in job market demand and employment trends. EU Countries Covered: Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the dataset to identify trends in job postings, understand competitive hiring practices, and optimize their recruitment strategies based on current market conditions. Labor Market Analysis: Analysts and policymakers can leverage the data to study labor market trends, identify skill gaps, and evaluate employment opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive list of job openings, tailored to their skills and preferred locations, facilitating a more efficient and targeted job search process. Strategic Workforce Planning: Companies can gain insights into the availability of talent in various regions, helping them make informed decisions about expanding operations or setting up new offices. Techsalerator’s Job Openings Data in Europe provides a crucial resource for understanding the dynamic European job market. By offering detailed, up-to-date information on job postings across multiple sectors and regions, it supports effective decision-making for businesses, job seekers, and market analysts alike.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Techsalerator’s Job Openings Data in Asia offers an extensive and meticulously compiled dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a thorough view of employment opportunities across the Asian continent. This dataset aggregates job postings from a wide array of sources on a daily basis, ensuring users have access to the most current and comprehensive collection of job openings available throughout Asia.

Key Features of the Dataset: Extensive Coverage:

The dataset consolidates job postings from diverse sources, including company career websites, job boards, recruitment agencies, and professional networking platforms. This broad coverage ensures that the dataset includes a wide range of job opportunities across multiple channels. Daily Updates:

Job posting data is aggregated and updated daily, providing users with real-time insights into the job market. This frequent updating ensures that the information reflects the latest job openings and market trends. Sector-Specific Data:

The dataset categorizes job postings by industry sectors such as technology, finance, healthcare, education, manufacturing, and more. This categorization allows users to analyze job market trends and opportunities within specific industries. Regional Breakdown:

Detailed information is provided on job openings across various countries, regions, and cities in Asia. This regional breakdown helps users understand job market dynamics and opportunities in different geographic locations. Role and Skill Insights:

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

Users can access details about the companies posting job openings, including company names, industries, and locations. This information is valuable for understanding which companies are hiring and where the demand for talent is concentrated. Historical Data:

The dataset may include historical job posting data, enabling users to analyze trends and changes in the job market over time. This feature supports longitudinal studies and comparative analysis. Asian Countries Covered: Afghanistan Armenia Azerbaijan Bahrain Bangladesh Bhutan Brunei Cambodia China Cyprus Georgia India Indonesia Iran Iraq Israel Japan Jordan Kazakhstan Kuwait Kyrgyzstan Laos Lebanon Malaysia Maldives Mongolia Myanmar (Burma) Nepal North Korea Oman Pakistan Palestine Philippines Qatar Saudi Arabia Singapore South Korea Sri Lanka Syria Taiwan Tajikistan Thailand Timor-Leste (East Timor) Turkey Turkmenistan United Arab Emirates Uzbekistan Vietnam Yemen Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the data to identify emerging hiring trends, understand competitive hiring practices, and refine their recruitment strategies based on real-time market conditions. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill shortages, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive list of job openings tailored to their skills and preferred locations, making their job search process more efficient and targeted. Strategic Workforce Planning: Companies can gain insights into the availability of talent in various Asian countries, assisting in decisions related to market expansion, office locations, and talent acquisition. Techsalerator’s Job Openings Data in Asia is a critical resource for gaining a detailed understanding of the diverse and dynamic job markets across the continent. By providing up-to-date and extensive information on job postings, it supports informed decision-making for businesses, job seekers, and market analysts.

Employment Rate in Mexico decreased to 97.46 percent in April from 97.78 percent in March of 2025. This dataset provides - Mexico Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Commercial services, the activities operating within the private sector, are attracted to markets according to the population of the area they serve and the level of market income. The growth rates for wholesaling varied regionally, with the higher rates in British Columbia, Alberta, Ontario and Quebec contrasting with those in Saskatchewan, Manitoba and the Atlantic provinces. The highest rates of growth occurred in British Columbia and Alberta, and in or near Toronto, Ottawa and Montréal. These are some of the places with the highest per capita incomes, and generally the places with highest rates of population growth during this period. The highest growth rates are found in small peripheral cities, which had a very small employment base in 1986 (for example, La Baie, Quebec, and Port Alberni and Fort St. John, British Columbia).

The growth rate in Surat was approximately 5.9 percent during 2014 and 2016, while the growth rate in Bangalore was around 3.7 percent during the same time period. Surat ranked 44 among the 300 largest metro areas globally.

Looking to explore job market trends, salary ranges, or hiring patterns for December 2023? Our latest dataset, sourced directly from Naukri.com, is your gateway to understanding the intricacies of India job market.

With thousands of job listings meticulously scraped and organized, this dataset provides actionable insights for researchers, data enthusiasts, and hiring professionals alike.

What Does This Dataset Offer?

Comprehensive Job Listings: Spanning diverse industries, roles, and experience levels. Key Job Details: Includes fields like job title, company name, location, experience required, salary offered, and skills mentioned. Timestamped Data: Reflecting the most current job trends and openings for December 2023. Structured Format: Ready-to-use CSV files that make it easy for analysis, machine learning, or visualization projects.

Why Use This Dataset?

1. Understand Hiring Trends: Uncover which industries are hiring the most and what roles are in demand.
2. Analyze Salaries: Compare salary ranges across different cities and roles.
3. Track Skills Demand: Gain insights into the most sought-after skills in the Indian job market.
4. Power AI Models: Use the data to train machine learning models for job recommendation systems or market analysis.

Ideal For: Data scientists and analysts exploring labor market trends. Businesses aiming to optimize recruitment strategies. Academics researching employment patterns and skill evolution. Developers building job search or recommendation platforms.

This dataset is a treasure trove of insights for anyone diving into India dynamic job market. Get your hands on the data and unlock new opportunities for analysis or innovation.If you are looking for customized datasets or need real-time job data for your projects, PromptCloud has you covered. Our web scraping services are tailored to deliver clean, structured data from complex sources, empowering businesses and researchers to achieve their goals seamlessly. Reach out to PromptCloud and take your data-driven initiatives to the next level! https://www.promptcloud.com/contact/

The pattern of growth rates for public administration shows the most distinctive pattern of change. There were substantial declines, with more than half of the cities losing employment during the period 1986 to 1996. The federal capital (Ottawa) and the provincial capitals Halifax and Winnipeg suffered the greatest losses. The highest rates of growth occurred in coastal British Columbia and in small cities on the fringes of Toronto and Montréal.