As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

Managers earned on average the highest monthly salary in Norway. In 2022, people with a manager position earned over 78,000 Norwegian kroner on average on a monthly basis. Professionals were the occupational group with the second highest average monthly salary, followed by technicians, associate professors, and people employed in the armed forces. The lowest average salaries in Norway that year were found among elementary occupations.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

As of 2024, surgeons ranked first in the list of highest-paying professions in Russia with an average monthly salary of 386,000 Russian rubles. Judges ranked second, earning 324,000 Russian rubles on average per month.

A comprehensive dataset of top occupations for Green Card sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest data on Green Card sponsorship trends and employer behavior.

Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

Dataset Features

Job Listings & Salaries: Access structured salary data from top job platforms, including job titles, company names, locations, salary ranges, and compensation types. Employer & Industry Insights: Extract company-specific salary trends, industry benchmarks, and hiring patterns. Geographic Pay Disparities: Compare salaries across different regions, cities, and countries to identify location-based compensation trends. Job Market Trends: Monitor salary fluctuations, demand for specific roles, and hiring trends over time.

Customizable Subsets for Specific Needs Our Salary Dataset is fully customizable, allowing you to filter data based on job titles, industries, locations, experience levels, and salary ranges. Whether you need broad market insights or focused data for recruitment strategy, we tailor the dataset to your needs.

Popular Use Cases

Workforce Planning & Talent Acquisition: Optimize hiring strategies by analyzing salary benchmarks and compensation trends. Market Research & Competitive Intelligence: Compare salaries across industries and competitors to stay ahead in talent acquisition. Career Decision-Making: Help job seekers evaluate salary expectations and identify high-paying opportunities. AI & Predictive Analytics: Use structured salary data to train AI models for job market forecasting and compensation analysis. Geographic Expansion & Business Strategy: Assess salary variations across regions to plan business expansions and remote workforce strategies.

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Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

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Graph and download economic data for Employed full time: Wage and salary workers: Computer programmers occupations: 16 years and over (LEU0254477100A) from 2000 to 2024 about computers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

The Census Bureau has created a special subset file from the 1990 Census of Population and Housing data designed to meet the needs of Equal Employment Opportunity (EEO) and affirmative action planning. It contains detailed 1990 Census data dealing with occupation and educational attainment for the civilian labor force, various racial groups, and the Hispanic population. The file consists of four tabulations of the United States civilian labor force. They present EEO data similar to those in the CENSUS OF POPULATION AND HOUSING, 1990 [UNITED STATES]: EQUAL EMPLOYMENT OPPORTUNITY (EEO) FILE (ICPSR 9929), but are expanded to include occupation data by education level, industry group, and earnings. Total population and unemployment data are also available. They are referred to as Tables P1-P4. Table P1 lists occupation by education by sex by race and Hispanic origin. Table P2 lists occupation by earnings by sex by race and Hispanic origin. Table P3 lists occupation by industry by sex by race and Hispanic origin. Table P4 lists population and unemployment by sex by race and Hispanic origin. The collection includes four United States files and 51 separate files, one for each state and Washington, DC. Each state file contains statistics for the state, each county, Standard Metropolitan Statistical Areas (SMSAs), and places with a population of 50,000 or more.

Abstract

Looking for a job as Data Analyst? Maybe this dataset can help you.

About this dataset

Amidst the pandemic many people lost their jobs, with this dataset it is possible to hone the job search so that more people in need can find employment. This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions, with features such as: - Salary Estimate - Location - Company Rating - Job Description - and more.

How to use

• Find the best jobs by salary and company rating
• Explore skills required in job descriptions
• Predict salary based on industry, location, company revenue
• Your kernel can be featured here!
• Data Engineer Jobs
• Business Analyst Jobs
• Data Scientist Jobs
• More Datasets

Acknowledgements

If you use this dataset, please support the author.

License

License was not specified at the source

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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

A comprehensive dataset of top job titles for Green Card sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest data on Green Card sponsorship trends and employer behavior.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B24021 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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

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

As of the 2023/24 academic year, graduates from the Massachusetts Institute of Technology (MIT) had a starting salary of 110,200 U.S. dollars, and a mid-career salary of 196,900 U.S. dollars. Top universities in the United States One of the top universities in the United States, Harvey Mudd College, is located in Claremont, California. Not only do graduates earn a high salaries after graduation, they also pay the most. In the academic year of 2020-2021, Harvey Mudd College was one of the most expensive school by total annual cost. The best university in the United States in 2021 belonged to the University of California, Berkeley. The Ivy League The Ivy League is a group of eight private universities in the Northeastern United States. It is not only a collegiate athletic conference, but also a group of highly respected academic institutions. They are usually regarded as the best eight universities in the United States and the world. They are extremely selective with their admissions process. However, these universities are extremely expensive to attend. Despite the high price tag, students who graduate from Princeton University have the highest early career salary out of all Ivy League attendees in 2021. This is compared to the overall expected starting salaries of recent college graduates across the United States, which was less than 35,000 U.S. dollars.

Occupations are classified using the three digit National Occupational Classification (NOC) codes. Wages include: average hourly wage rate, average weekly wage rate, median hourly wage rate and median weekly wage rate.