About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset contains information of 22700+ Software Professionals with different features like their Salaries (₹), Name of the Company, Company Rating, Number of times Salaries Reported, and Location of the Company.

Extra Features Added: 1. Employment Status 2. Job Roles

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Roles Included:

Android Developer Android Developer - Intern Android Developer - Contractor Android Developer Contractor Senior Android Developer Android Software Engineer Android Engineer Android Applications Developer - Intern Android Applications Developer Android App Developer - Intern Senior Android Developer and Team Lead Android Tech Lead Product Engineer (Android) Software Engineer - Android Android Software Developer Android Software Developer - Intern Senior Android Developer Contractor Junior Android Developer - Intern Junior Android Developer Android Applications Developer - Contractor Android App Developer Lead Android Developer Android Engineer - Intern Sr. Android Developer Senior Android Engineer Senior Software Engineer - Android Android - Intern Android Android & Flutter Developer - Intern Associate Android Developer Senior Android Applications Developer Android Developer Trainee Sr Android developer Android Trainee Android Trainee - Intern Trainee Android Developer Android Lead Android Lead Developer Android Development - Intern Android Development Android Team Lead Senior, Android Developer Lead Android Engineer Tech Lead- Android Applications Developer Senior Android Software Developer Full Stack Android Developer Android Framework Developer Android Architect Android & Flutter Developer Senior Software Engineer, Android Android App Development Sr Android Engineer Android Team Leader Android Technical Lead SDE2(Android) Web Developer/Android Developer - Intern Android Applications Develpoers Android Platform Developer - Intern Android Test Engineer Senior Engineer - Android Android Framework Engineer Game Developer ( Android, Windows) Android Testing Senior Software Engineer (Android/Mobility) Ace - Android Development Software Developer (Android) - Intern Android Mobile Developer Android and Flutt...

According to the survey, Erlang and Elixir are the programming languages that are associated with the highest salaries worldwide in 2024, with an average of around *** and ** thousand U.S. dollars respectively.

Senior Executive was the highest-earning software development job worldwide as of 2024, with the average annual salary amounting to around *** thousand U.S. dollars. Developer advocate and engineering manager were the other developer jobs with high salaries. Software developer A software developer, or more commonly known as a programmer or a coder, is a person who creates software by using computer languages. There are two main types of software developers: application developer – those who design computer or mobile applications – and system developer – those who focus on operating systems-level software and network distribution software. In 2021, there were around **** million software developers worldwide. Programming languages A programming language is a formal language that instruct a computer or computing device to perform certain tasks. Languages that software developers use to write code are called “high-level languages”; these can be complied into a “low-level language” which can be understood by the computer hardware. Programming languages that are most used by developers include JavaScript, HTML/CSS, SQL, and Python. Clojure, F# and Elixir, on the other hand, are the languages associated with the highest salaries.

Salary by Profession and Country Over Time

Salary Differences by Country and Profession

By Kelly Garrett [source]

About this dataset

This dataset contains survey responses from 882 data professionals from 46 countries who took part in the 2021 Global Data Professional Salary Survey. Our goal was to understand how much database administrators, data analysts, data architects, developers and data scientists make across the world in 2017-2021.

The survey covers three years of salary trends, allowing you to compare and contrast movements over time. It also includes an optional postal code field which can be used to identify global regions with specific salary trends. In addition, all questions asked this year were also asked in 2017 and 2018 so that you can easily track changes in compensation over three years.

The spreadsheet contains anonymized responses which are provided as public domain making it available for any purpose without attribution or mention of anyone else. With this dataset at your disposal you'll have access to the detailed salary information needed to make informed decisions about your career development!

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by familiarizing yourself with the columns in this dataset. The columns range from age of respondent to country of residence. It also includes salary information for each year (average annual income for 2017, 2018, and 2019). Read through each column header carefully to understand what you're looking at.

• Explore some basic summary statistics about the sample group such as median salary levels by profession or average age by nationality are interesting ways to get acquainted with this data set quickly. Excel's native statistical tools may be used here if you're using an excel file version as your source material; otherwise, you can use any programming language or statistics software that supports importing an exportable CSV (Comma Separated Values) format file or conversion thereof into something manipulable form like a spreadsheet or table structure within your preferred platform..

• You'll then want to identify which factors might be influencing salaries such as experience level, gender and geographical location etc., and attempt some correlation testing between those features against salaries across different job roles or countries over time - where possible without having external datasets available terms of area data points matching up perfectly between thematic dimensions presented within the Respondents' Survey Results tab.. Subsets may also prove relevant when carrying out deeper statistical testing—for example isolating particular participation sets like Ireland alone versus looking at just Europe/Middle East/Africa region altogether..

• Finally look at how these factors have changed over time - it's worth bearing in mind that seasonality might play a role here too depending on where respondents originally reside so it could still be relevant if larger trends towards comparing yearly cohorts differs more widely than expected based purely national economic condition context changes during particular quarters throughout those periods tracked in our findings report � comparison purposes if looking country-by-country instead just individual profiles without taking overall stimulant effects into account e.g higher education qualifications among ~2 yr cohorts vs ~3 yr ones across different populations: Comparing annual amounts doled out employers making ultra-quick transitioning easier tracking changes alone isn't feasible because they're normalized

Research Ideas

• Analyzing regional salary gaps amongst data professionals within the same country, or between countries.
• Evaluating trends in salary rates over time by reviewing changes in year over year responses.
• Generating employer profiles by comparing the salary range of employees at different organizations and industries, as well storing demographic info of individuals who participated in the survey (i.e age range, gender etc)

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2019_Data_Professional_Salary_Survey_Responses.csv

File: Data_Professional_Salary_Survey_Responses.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Kelly Garrett.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

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

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

This dataset merges player per-game and advanced statistics for the NBA's 2022-23 season with player salary data, creating a comprehensive resource for understanding the performance and financial aspects of professional basketball players. The dataset is the result of web scraping player salary information from Hoopshype, and downloading traditional per-game and advanced statistics from Basketball Reference.

Key Features:

• Player Information: Player name, team(s) played for during the season.
• Per Game Statistics: A wide array of per-game statistics, including points scored (PPG), assists (APG), rebounds (RPG), steals (SPG), blocks (BPG), and more.
• Shooting Efficiency: Metrics like field goal percentage (FG%), three-point percentage (3P%), two-point percentage (2P%), and free throw percentage (FT%) for assessing scoring efficiency.
• Advanced Statistics: A wide array of advanced metrics such as value over replacement player (VORP), win shares (WS) and true shooting percentage (TS%)
• Salaries: The financial aspect of the dataset includes player salaries for the 2022-23 season, offering insights into player earning

Potential Uses:

• Player Performance Analysis: Analysts can evaluate player performance in correlation with their earnings, identifying players who provide strong value for their salaries.
• Team Budgeting and Strategy: Teams can use this dataset to assess the financial implications of player acquisitions, trades, and salary cap management.
• Player Earnings Insights: The dataset offers insights into the income distribution of NBA players, helping fans and analysts understand the financial side of professional basketball.
• Data-Driven Decisions: Data scientists can leverage this combined dataset for predictive modeling and machine learning applications, including salary predictions and player valuation.

Acknowledgements: Basketball Reference, Hoopshype

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.

According to a report released in July 2020 in Vietnam, tech management positions such as chief technology officer (CTO) or chief information officer (CIO) had the highest average salary per month. People in this position usually earned around ***** U.S. dollars monthly. The second highest-paid position was technical director/ engineering manager with an average salary of ***** U.S. dollars per month. Bridge system engineer stood at the bottom of the top 10, earning close to ***** U.S. dollars monthly on average.

This data was collected by the team https://dou.ua/ . This resource is very popular in Ukraine. It provides salary statistics, shows current vacancies and publishes useful articles related to the life of an IT specialist. This dataset was taken from the public repository https://github.com/devua/csv/tree/master/salaries . This dataset will include the following data for each of the developer: salary, position (f.e. Junior, Middle), experience, city, tech (f.e C#/.NET, JavaScript, Python). I think this dataset will be useful to our community. Thank you.

Context

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Structure of the Dataset

https://i.imgur.com/G8GwKx5.png" alt="">

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Cover Photo by rupixen.com on Unsplash

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.

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

In 2023, the growth rate of the average salary of a software developer in Russia reached over ** percent in Russian ruble terms. In U.S. dollar terms, the salary declined by more than ***** percent from the previous year.

Graph and download economic data for Employed full time: Wage and salary workers: Computer programmers occupations: 16 years and over: Women (LEU0254690700A) from 2000 to 2024 about computers, occupation, full-time, females, salaries, workers, 16 years +, wages, employment, and USA.

Turkey Wage & Salary Index: TS: IC: Computer Programming data was reported at 230.921 2010=100 in Dec 2017. This records an increase from the previous number of 222.158 2010=100 for Sep 2017. Turkey Wage & Salary Index: TS: IC: Computer Programming data is updated quarterly, averaging 109.719 2010=100 from Mar 2005 (Median) to Dec 2017, with 52 observations. The data reached an all-time high of 230.921 2010=100 in Dec 2017 and a record low of 34.823 2010=100 in Mar 2005. Turkey Wage & Salary Index: TS: IC: Computer Programming data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.G048: Gross Wage and Salary Index: NACE Rev 2: 2010=100: by Trade and Services. Rebased from 2010=100 to 2015=100 Replacement series ID: 403908797

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

In 2023, Polish programmers who specialized in embedded programming and were hired on an employment contract earned the most, at ***** zloty net per month.

The Occupational Employment Statistics (OES) program conducts a semiannual survey designed 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 for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. The Bureau of Labor Statistics produces occupational employment and wage estimates for approximately 415 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and selected 5- and 6-digit North American Industry Classification System (NAICS) industrial groups. The OES program surveys approximately 200,000 establishments per panel (every six months), taking three years to fully collect the sample of 1.2 million establishments. To reduce respondent burden, the collection is on a three-year survey cycle that ensures that establishments are surveyed at most once every three years. The estimates for occupations in nonfarm establishments are based on OES data collected for the reference months of May and November. The OES survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OES survey make these estimates possible. The OES features several arts-related occupations, particularly in the Arts, Design, Entertainment, Sports, and Media Occupations group (Standard Occupational Classification (SOC) code 27-0000). Several featured occupation groups include the following: Art and Design Workers (SOC 27-1000) Art Directors Fine Artists, including Painters, Sculptors, and Illustrators Multimedia Artists and Animators Fashion Designers Graphic Designers Set and Exhibit Designers Entertainers and Performers, Sports and Related Workers (SOC 27-2000) Actors Producers and Directors Athletes Coaches and Scouts Dancers Choreographers Music Directors and Composers Musicians and Singers Media and Communication Workers (SOC 27-3000) Radio and Television Announcers Reports and Correspondents Public Relations Specialists Writers and Authors Data for years 1997 through the latest release and can be found on the OES Data page. Also, see OES News Releases sections for current estimates and news releases. Users can analyze the data for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. As well, OES Charts are available. Users may also explore data using OES Maps. If preferred, data can also be accessed via the Multi-Screen Data Search or Text Files using the OES Databases page.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer programmers occupations: 16 years and over: Men (LEU0254637300A) from 2000 to 2024 about second quartile, computers, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.