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.

Explore the progression of average salaries for graduates 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 Analytics And Modeling relative to other fields. This data is essential for students assessing the return on investment of their education in Analytics And Modeling, providing a clear picture of financial prospects post-graduation.

Envestnet | Yodlee's Payroll 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 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.

What are data science professionals really earning in 2025? This dataset offers a comprehensive look into global salary trends for roles in Data Science, Machine Learning, and Artificial Intelligence.

Carefully curated using a combination of market research and publicly available data sources—including the AIJobs salary survey (CC0 license), 365DataScience, Payscale, KDnuggets, ZipRecruiter, and others—this dataset reflects real-world compensation patterns from around the globe.

Why This Dataset Matters

Whether you're a data scientist, AI practitioner, student, recruiter, or industry researcher, this dataset is built to support:

• Salary prediction and ML modeling
• Global market benchmarking
• Career decision-making and negotiation
• Remote work trend analysis
• Business intelligence dashboards and visualizations

Data Sources

• aijobs.net Salary Dataset (CC0)
• 365datascience.com
• Payscale
• KDnuggets
• ZipRecruiter
• Wellfound (AngelList)

Glassdoor Data for U.S. Salary Intelligence, Executive Compensation & HR Strategy Glassdoor Data is one of the most actionable and trusted sources of alternative data for understanding salary benchmarks, executive compensation, and company-level payroll dynamics. This Glassdoor Data product provides granular salary insights across thousands of U.S. companies, based directly on job titles, experience levels, and compensation submissions. Each record includes: • Company name • Job title • Location • Years of experience • Submission count • Total pay • Base and additional pay • Scrape timestamp • Source URL The dataset is designed to offer transparent, real-world compensation intelligence for roles across industries and seniority levels—without relying on external company datasets or third-party matching. Whether you're benchmarking pay levels, building compensation bands, or modeling payroll scenarios, this Glassdoor salary data delivers raw, high-signal insights to support strategic decisions across HR, finance, and analytics teams. This dataset is ideal for compensation strategists, HR teams, financial analysts, and market researchers who require consistent, transparent, and scalable compensation intelligence to support pay benchmarking, cost modeling, or workforce evaluation. Use Cases: What Problems This Glassdoor Data Solves Whether you’re building a salary model, evaluating leadership compensation trends, or enhancing your payroll benchmarking strategy, Canaria’s Glassdoor data replaces assumptions with structured, evidence-based insights. Compensation Benchmarking & Strategy • Benchmark average salary ranges, executive pay, and bonus structures by industry • Understand pay differences by company size, structure, and market positioning • Compare compensation levels across roles, sectors, and seniority levels • Support equitable pay band development and validate DEI-focused compensation initiatives • Track salary progression over time across different industries and geographies Financial Intelligence & Valuation Modeling • Integrate salary and payroll data into operating cost and DCF models • Analyze compensation-to-revenue ratios across public and private companies • Use Glassdoor executive compensation signals to estimate fiscal discipline and growth maturity • Evaluate total labor cost benchmarks across sectors to support investment or M&A models • Support startup valuation modeling using real-world salary and review signals HR Analytics & Payroll Planning • Calibrate compensation plans, equity offers, and incentive structures based on real benchmarks • Align workforce planning with external salary and benefits trends • Compare employee sentiment with compensation ratings to monitor retention risk • Understand compensation differentials across company size brackets and industries • Evaluate which employers attract and retain talent based on pay satisfaction reviews Company Analysis & Leadership Signals • Track CEO and leadership pay benchmarks to evaluate executive strategy alignment • Use Glassdoor scores and review counts to evaluate employee morale and culture • Analyze patterns in rating trends, review frequency, and compensation satisfaction • Compare compensation strategies across different growth stages and business models • Detect early warning signals through declining ratings or increasing negative reviews Who Uses Canaria’s Glassdoor Data? • HR & People Analytics teams refining salary structures • Finance and Operations teams modeling payroll scenarios • Private Equity, VC, and Corporate Strategy teams estimating workforce cost risk • Compensation Consultants benchmarking roles by industry and geography • BI and Research Analysts tracking labor cost trends and employer sentiment • KYC and corporate due diligence workflows integrating public employee reviews Summary Canaria’s Glassdoor Data product delivers verified, structured, and independently sourced salary and compensation intelligence for U.S. companies. With a focus on executive pay, workforce sentiment, and real-world salary benchmarks, it equips HR, finance, and strategy teams with the tools needed to model, analyze, and optimize compensation decisions.

From benchmarking CEO pay to understanding employee satisfaction and cost structures, this dataset supports decision-making across compensation strategy, HR planning, and company evaluation—at scale.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligence — such as addresses, coordinates, hours, categories, and ratings — which is fully matchable with our company datasets for powerful ...

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 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.

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.

United States - Employed full time: Wage and salary workers: Model makers and patternmakers, metal and plastic occupations: 16 years and over: Men was 0.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Model makers and patternmakers, metal and plastic occupations: 16 years and over: Men reached a record high of 14.00000 in January of 2000 and a record low of 0.00000 in January of 2022. 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, metal and plastic occupations: 16 years and over: Men - last updated from the United States Federal Reserve on July of 2025.

LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligen...

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.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for quantitative methods and modeling related to computer science in the U.S.

The demand for big data talent is rapidly increasing with the growth of the big data industry. However, there has been limited research on what employers seek in recruiting big data talent. This paper aims to apply labor market segmentation theories to the big data labor market and develop a theoretical framework to analyze the distribution of big data talent in different labor market segments. Furthermore, we develop a salary determination model to explain wage differentials. An empirical analysis is conducted using online job advertisements from a Chinese recruitment website to investigate the labor market for big data talent in China. Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. Different types of enterprises have different requirements for individual characteristics and offer various levels of big data job positions. Furthermore, our results reveal that individual, job-related and organizational characteristics are all significant predictors of salaries. These findings can provide particularly useful insights for organizations and managers in the big data industry.

Explore the progression of average salaries for graduates in Systems Modeling And Analysis Emphasis 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 Emphasis Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Systems Modeling And Analysis Emphasis Statistics, providing a clear picture of financial prospects post-graduation.

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

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.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Model makers and patternmakers, wood occupations: 16 years and over (LEU0254571600A) from 2000 to 2011 about second quartile, occupation, wood, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

This Excel file contains four tabs based on data hand-collected from geographical peer institutions to compare library salaries. In some instances, library salaries where provided directly to the researcher, but in most instances, the researcher relied on open data regarding state salaries to estimate and include data. This data should not be taken as perfect data, but as best estimated data using current staff member positions and names combined with university or college pay scales. Salary Comparisons: The first tab of this spreadsheet contains hand-keyed data comparing estimated salaries for library deans, library directors, librarians, library assistant IIs, and library assistant Is at geographical peer institutions. Salary Model: The second tab of this spreadsheet contains preliminary work to create a multilinear regression model to determine salary starting points at geographical peer institutions in the state of Oklahoma. Color Coded Salary Model: The third tab of this spreadsheet contains salary averages, position ranking within each library from 1-7 with 1 representing those closest to a library assistant position and 7 representing library deans or dean-type roles, and Excel formulas to represent each geographical peer on a line graph by color. Graphs: The fourth and final tab of this spreadsheet includes all graphs generated within the spreadsheet itself. The report associated with this dataset encompasses preliminary research, data, and statistics collected to discuss the role of McFarlin Library in the University of Tulsa's 2022 strategic initiatives. Data was gathered through publicly-available sources, such as the U.S. News and World Report rankings of colleges and universities within the United States, information gathered from websites of libraries within that ranking, and cost estimations from publicly-available sources. This project includes unpublished documents containing rudimentary ideas regarding the role of McFarlin Library in the University of Tulsa’s Strategic Initiatives. These materials do not represent the views of the University of Tulsa or McFarlin Library at the University of Tulsa, nor does it represent implementation steps, legitimate suggestions, or advice. Those who download and consult this document do so with the understanding that it has not been implemented or consulted by the University of Tulsa’s administration.

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.