This dataset contains information on the relationship between work experience (in months) and corresponding monthly salaries (in thousand dollars) of employees across various industries. It is designed to help data enthusiasts and aspiring data scientists practice linear regression techniques by analyzing and modeling salary predictions based on experience.

Explore the progression of average salaries for graduates in Computational Modeling Data Analytics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computational Modeling Data Analytics relative to other fields. This data is essential for students assessing the return on investment of their education in Computational Modeling Data Analytics, providing a clear picture of financial prospects post-graduation.

Envestnet | Yodlee's Salary Data Panel captures de-identified payroll information to deliver valuable employment insights, such as a company's wage costs, seasonal performance, headcount, hiring, layoffs, and more.

De-identified payroll data analytics for major employers gives decision makers insight into employment trends across many industries. The payroll product includes 1000+ employers and data can be used for company specific or macro purposes. - 4800+ employers tagged - Frequency of payroll identified (i.e. weekly, bi-weekly)
- Data at user and account level to allow for cohort analysis (e.g. Macys likely to lose 10% of revenue due to unemployment within their cohort)

New Features - Mapping to Category codes and Employer Dependency Scoring Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Explore the progression of average salaries for graduates in Simulation, Modeling, And Applied Cognitive Science from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Simulation, Modeling, And Applied Cognitive Science relative to other fields. This data is essential for students assessing the return on investment of their education in Simulation, Modeling, And Applied Cognitive Science, providing a clear picture of financial prospects post-graduation.

The motivation behind analyzing salary data is to gain insights into compensation trends, identify factors that influence pay, and understand disparities across industries, locations, or job roles. For businesses, this analysis is crucial in shaping competitive compensation packages, attracting top talent, and ensuring fair pay practices. Additionally, individuals can benefit from understanding how their salaries compare to industry standards, aiding in negotiation strategies.

Context With increasing attention on pay transparency and equity, salary data has become a critical dataset for human resources departments, economists, and policymakers. Companies and industries alike need to assess compensation against benchmarks, inflation, and the evolving job market. Salary datasets often contain variables such as job titles, experience levels, education, locations, and industries, which are essential in determining pay structures. This analysis allows for a deeper dive into trends like gender pay gaps, regional disparities, and the impact of education or experience on earnings.

For the Kaggle community, salary datasets provide rich opportunities for performing exploratory data analysis, statistical modeling, and predictive analytics. It serves as a hands-on opportunity to practice data wrangling, feature engineering, and model building, especially in the realm of HR analytics.

Description This CSV file contains anonymized company salary data across various industries, roles, and locations. The dataset includes key variables such as:

Job Title: The role of the employee (e.g., Data Analyst, Software Engineer). Years of Experience: Number of years the employee has been in the workforce or industry. Education Level: The highest degree obtained by the employee (e.g., Bachelor's, Master's). Location: City or country where the employee works. Industry: The sector in which the company operates (e.g., Finance, Technology). Annual Salary: The employee’s yearly earnings, including bonuses or incentives. Gender: Gender identification of the employee (if available). Remote Work Percentage: The percentage of work conducted remotely, which may influence salary based on location independence. The dataset is perfect for understanding how salaries vary by job role, region, industry, and experience level. It can also be used to uncover trends such as salary growth over time, the impact of education or certifications on compensation, or potential gender pay gaps. Through data visualization, predictive models, and regression analysis, users can extract meaningful insights that could inform corporate strategy, HR policies, or even career decisions.

Explore the progression of average salaries for graduates in Engineering Focus In Computational Modeling from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Engineering Focus In Computational Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Engineering Focus In Computational Modeling, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Systems Modeling And Analysis (A Subfield Of Statistics) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Systems Modeling And Analysis (A Subfield Of Statistics) relative to other fields. This data is essential for students assessing the return on investment of their education in Systems Modeling And Analysis (A Subfield Of Statistics), providing a clear picture of financial prospects post-graduation.

Analysis of ‘Data Professionals Salary - 2022’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamsouravbanerjee/analytics-industry-salaries-2022-india on 28 January 2022.

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

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 towards 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, Data Engineers in various cities across India (2022).

Acknowledgements

For more, please visit: https://www.glassdoor.co.in/

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

Explore the progression of average salaries for graduates in Data Science In Analytics And Modeling from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Science In Analytics And Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Data Science In Analytics And Modeling, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Computational Analysis & Modeling from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computational Analysis & Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Computational Analysis & Modeling, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Digital Modeling Technology from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Digital Modeling Technology relative to other fields. This data is essential for students assessing the return on investment of their education in Digital Modeling Technology, providing a clear picture of financial prospects post-graduation.

United States - Employed full time: Wage and salary workers: Model makers and patternmakers, wood occupations: 16 years and over: Women was 0.00000 Thous. of Persons in January of 2019, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Model makers and patternmakers, wood occupations: 16 years and over: Women reached a record high of 1.00000 in January of 2003 and a record low of 0.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Model makers and patternmakers, wood occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

Explore the progression of average salaries for graduates in Mathematical Biology; Mathematical Modeling Emphasis from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Mathematical Biology; Mathematical Modeling Emphasis relative to other fields. This data is essential for students assessing the return on investment of their education in Mathematical Biology; Mathematical Modeling Emphasis, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Statistical And Economic Modeling from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Statistical And Economic Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Statistical And Economic Modeling, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Molecular Modeling from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Molecular Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Molecular Modeling, providing a clear picture of financial prospects post-graduation.

Graph and download economic data for Employed full time: Wage and salary workers: Model makers and patternmakers, metal and plastic occupations: 16 years and over: Men (LEU0254622000A) from 2000 to 2024 about occupation, plastics, full-time, males, salaries, workers, metals, 16 years +, wages, employment, and USA.

This dataset contains the raw data behind three strategies that the Pay It Forward project team considered for estimating the cost of publishing an article in a scholarly journal.

Graph and download economic data for Employed full time: Wage and salary workers: Model makers and patternmakers, wood occupations: 16 years and over (LEU0254518200A) from 2000 to 2019 about occupation, wood, full-time, salaries, workers, wages, 16 years +, employment, and USA.

Data Overview WageScape's US job listings dataset offers real-time, forward-looking insights into the American labor market. Covering millions of job postings data from various industries and locations, it supports workforce analytics, economic forecasting, and strategic planning, enabling businesses to make data-driven decisions with salary data, skill taxonomy data, and comprehensive company data.

Data Enrichment The dataset includes enriched data with industry codes, title normalization, company normalization, geographic parsing, firmographic info, and compensation data, enhancing usability and accuracy. PrecisionPay™ technology offers detailed analysis of upcoming pay trends, providing businesses with deeper insights and precise data for effective recruiting, compensation planning, and organizational growth.

Main Attributes • Job Titles: Across various sectors and industries, supported by detailed job postings data. • Job Descriptions: Detailed roles and requirements based on recruiting data. • Salary data: Comprehensive pay information, including salary ranges, sourced through PrecisionPay™ technology. • Locations: From state-level to city-specific details, offering insights into company data. • Company Information: Name, size, revenue, and industry sectors, providing context to company data. • Posting Dates: Timeline of market activity reflecting trends in job postings data. • Job Requirements: Skills, education, and experience needed, informed by skill taxonomy data.

Coverage • Industries: Technology, healthcare, finance, manufacturing, retail, and more, analyzed through company data. • Geographical Reach: National coverage, including metropolitan areas, regional hubs, and smaller towns, enriched with job postings data.

Scale and Quality • Data Volume: Over 4 million job postings monthly, providing extensive recruiting data. • Hiring Organizations: Data from 6+ million organizations, supported by reliable company data. • High Precision: Rigorous validation for accuracy, ensuring dependable salary data.

Use Cases • Workforce Analytics: Analyze trends and dynamics using job postings data and recruiting data for HR decisions. • Economic Forecasting: Predict economic and labor market shifts based on company data and job postings data. • Talent Acquisition: Improve recruitment strategies with detailed recruiting data and insights from skill taxonomy data. • Market Research: Understand industry trends using enriched company data. • Strategic Planning: Inform long-term business strategies with comprehensive job postings data and salary data.

Data Accessibility • Delivery Channels: Available through Data-as-a-Service (DaaS), with access to recruiting data and job postings data. • Customizable Reports: Tailored to specific business needs, incorporating skill taxonomy data. • Integration: Seamless integration into existing systems, supporting detailed company data analysis.

Key Benefits • Real-Time Insights: Up-to-date job information for timely decisions based on job postings data. • Forward-Looking Data: Predict future labor market trends with enriched company data and salary data. • Comprehensive Coverage: Extensive industry and geographic data, including detailed recruiting data. • High Quality and Scale: Millions of postings monthly for robust analysis, supported by skill taxonomy data. • Actionable Insights: Enhance job modeling and workforce strategies with high-quality company data.

Key Points WageScape's dataset is essential for businesses to understand the labor market deeply. With extensive coverage and high-quality company data, it empowers organizations to optimize workforce strategies and maintain a competitive edge, leveraging the latest job postings data, salary data, and skill taxonomy data.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for analytics and modeling in the U.S.