In 2024, people working in IT management in the United States, earned an average annual salary worth around 168 thousand U.S. dollars. Software developers and project managers all reported being paid on average over 120 thousand U.S. dollars. Despite nearly all categories saw a year-on-year increase in annual compensation, IT support and help desk technicians saw a decrease compared to the previous year

Dataset Card for Data Science Job Salaries

  Dataset Summary







  Content

Column Description

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… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

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.

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

This graph displays the twenty largest occupation groups in the United States as of May 2023, ranked by annual mean wage. The annual mean wage among the 7.7 million retail sales workers in the U.S. stood at 34,520 U.S. dollars in 2023.

In 2024, senior executives in the IT market reported the highest total annual compensation, with a median of over 127,388 thousand U.S. dollars. Developer advocates, managers, and developer experience engineers earned well over 100 thousand U.S. dollars annually. Site reliability engineers and cloud infrastructure engineers had median earnings of one thousand and 97 thousand U.S. dollars, respectively. At the lower end of the spectrum, academic researchers, front-end developers, and students earned less than 50 thousand U.S. dollars in total annual compensation.

Graph and download economic data for Employed: Paid below prevailing federal minimum wage: Wage and salary workers: Transportation and material moving occupations: 16 years and over (LEU0204843900A) from 2000 to 2024 about paid, occupation, materials, minimum wage, salaries, workers, transportation, 16 years +, federal, wages, employment, and USA.

This occupational wage dataset is based on Occupational Employment Statistics (OES) survey that captures 52,000 businesses. This particular dataset is on healthcare practitioners, technical occupation and healthcare support occupation. The other data set in this series include healthcare support occupations.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

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

Graph and download economic data for Employed full time: Wage and salary workers: Management, professional, and related occupations: 16 years and over: Women (LEU0254684800Q) from Q1 2000 to Q4 2024 about management, occupation, professional, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Office and administrative support occupations: 16 years and over: Men (LEU0254659000Q) from Q1 2000 to Q4 2024 about administrative, second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

The statistic depicts the salaries of IT professionals by region in the United States as of October 2010. The average salary of IT professionals in New England amount to 80.8 thousand U.S. dollars.

Graph and download economic data for Employed: Workers paid hourly rates: Private wage and salary workers: Professional and technical services industries: 16 years and over (LEU0204839800A) from 2000 to 2024 about paid, professional, salaries, workers, hours, 16 years +, wages, services, private, employment, industry, rate, and USA.

About the Dataset

Dataset Name: Salary Prediction Dataset

File Format: CSV

Rows: 100,000

Columns: 4

Overview

This dataset provides salary data based on years of experience, education level, and job role. It can be used for salary prediction models, regression analysis, and workforce analytics. The dataset includes realistic salary variations based on industry trends.

Columns Description

1. YearsExperience (float) – Number of years of experience (0 to 40 years).
2. Education Level (string) – The highest level of education attained. Categories include:
   • High School
   • Associate Degree
   • Bachelor's
   • Master's
   • PhD
3. Job Role (string) – Common job titles in the industry:
   • Software Engineer
   • Data Scientist
   • Product Manager
   • Marketing Specialist
   • Business Analyst
4. Salary (float) – The estimated annual salary in USD. The salary is influenced by experience, education level, and job role.

Potential Use Cases

• Salary Prediction Models – Train regression models to predict salaries based on experience and qualifications.
• Data Science & Machine Learning – Use this dataset for exploratory data analysis and feature engineering.
• Workforce Analysis – Analyze salary trends across job roles and experience levels.

How the Data Was Generated

The dataset was synthetically generated using a linear regression-based formula with added randomness and scaling factors based on job roles and education levels. While not real-world data, it closely mimics actual salary distributions in the tech and business industries.

Acknowledgments

This dataset is designed for research, learning, and data science practice. It is not collected from real-world surveys but follows statistical patterns observed in salary data.

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.

Women and Men in Spain: Average annual salary by occupation groups and period. Annual. National.

This statistic shows the annual base salaries of IT professionals worldwide in 2019. IT decision-makers in North America have the highest annual income in this chart, making a little over 133 thousand U.S. dollars a year in 2019.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by two-digit Standard Occupational Classification.

Graph and download economic data for Employed full time: Wage and salary workers: Cashiers occupations: 16 years and over (LEU0254497200A) from 2000 to 2024 about cashiers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.