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

🚀 Data Science Careers in 2025: Jobs and Salary Trends in Pakistan 🚀 Data Science is one of the fastest-growing fields, and by 2025, the demand for skilled professionals in Pakistan will only increase. If you’re considering a career in Data Science, here’s what you need to know about the top jobs and salary trends.

🔍 Top Data Science Jobs in 2025 1) Data Scientist Avg Salary: PKR 1.2M - 2.5M/year (Entry-Level), PKR 3M - 6M/year (Experienced) Skills: Python, R, Machine Learning, Data Visualization

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💡 Why Choose Data Science? High Demand: Every industry in Pakistan needs data professionals. Attractive Salaries: Competitive pay based on technical expertise. Growth Opportunities: Unlimited career growth in this field.

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🛠️ How to Get Started? Learn Skills: Focus on Python, SQL, Machine Learning, and Data Visualization. Build Projects: Work on real-world datasets to create a strong portfolio. Network: Connect with industry professionals and join Data Science communities.

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

Envestnet | Yodlee's Bank Statement 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

In 2024, the expected median starting salary for MBA graduates worldwide was ******* U.S. dollars. On the other hand, master's graduates in data analytics, business analytics, finance, and management were expected to have a median salary of ****** U.S. dollars.

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

Popular US workplace blog AskAManager (askamanager.org) sponsors an annual salary survey of blog readers. The 2023 survey collected data about industry, job function, title, annual salary, additional compensation, race, gender, remote/on-site requirements, education, location, and years' experience.

The dataset here features responses collected between April 11 and 28, 2023, and has some 16,000 responses. This version of the data set has employed several feature engineering techniques to group and cleanse data, convert the currency to USD values as of April 1, 2023, and add clarity to location data. In particular, US respondents were paired when possible with a metropolitan area.

Graph and download economic data for Wage and salary accruals per full-time equivalent employee (A4401C0A052NBEA) from 1929 to 2023 about accruals, full-time, salaries, wages, employment, GDP, and USA.

Explore the progression of average salaries for graduates in Systems Analysis (Information Systems [U.S. Equiv]) 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 Analysis (Information Systems [U.S. Equiv]) relative to other fields. This data is essential for students assessing the return on investment of their education in Systems Analysis (Information Systems [U.S. Equiv]), providing a clear picture of financial prospects post-graduation.

Analysis of ‘Maryland Average Wage Per Job (Constant 2012 Dollars): 2010-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3cc6185-dc9a-49c6-aee8-3da143aed711 on 26 January 2022.

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

Average Wage Per Job in Maryland and its Jurisdictions (Constant 2012 Dollars) from 2010 to 2018. Data source from U.S. Bureau of Economic Analysis (Table CA30), November 2019.

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

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in United States: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 4,740,400(3.72%) households where the householder is under 25 years old, 41,748,013(32.75%) households with a householder aged between 25 and 44 years, 46,534,687(36.50%) households with a householder aged between 45 and 64 years, and 34,459,765(27.03%) households where the householder is over 65 years old.
• In United States, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States median household income by age. You can refer the same here

Explore the progression of average salaries for graduates in System Analysis And Management U.S. Equiv. Applied Mathematics 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 System Analysis And Management U.S. Equiv. Applied Mathematics relative to other fields. This data is essential for students assessing the return on investment of their education in System Analysis And Management U.S. Equiv. Applied Mathematics, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Industrial Engineering - Systems Analysis And Planning (Us Equiv. - Computer Information Systems) 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 Industrial Engineering - Systems Analysis And Planning (Us Equiv. - Computer Information Systems) relative to other fields. This data is essential for students assessing the return on investment of their education in Industrial Engineering - Systems Analysis And Planning (Us Equiv. - Computer Information Systems), providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Accounting, Analysis And Audit U.S. Equiv Masters In Accounting 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 Accounting, Analysis And Audit U.S. Equiv Masters In Accounting relative to other fields. This data is essential for students assessing the return on investment of their education in Accounting, Analysis And Audit U.S. Equiv Masters In Accounting, providing a clear picture of financial prospects post-graduation.

Graph and download economic data for Employed full time: Wage and salary workers: Financial analysts occupations: 16 years and over (LEU0254476000A) from 2000 to 2019 about analysts, occupation, full-time, salaries, workers, financial, 16 years +, wages, employment, and USA.

Context

This dataset provides information on the average wage in various countries. Understanding the average wage in different countries is essential for economic analysis, benchmarking, and comparisons. Researchers, analysts, and policymakers can use this dataset to gain insights into global income disparities, labor market conditions, and economic trends.

Country_Gross Average Monthly Wages in 2020

The dataset comprises two primary columns: "Country" and "Gross Average Monthly Wages in 2020 (US$, at current Exchange Rates)." Each entry in the "Country" column represents a distinct country or region, while the corresponding entry in the "Gross Average Monthly Wages" column denotes the average earnings in US dollars for the specified location in the year 2020.

Development of Average Annual Wages

The "Development of Average Annual Wages" dataset, available on Kaggle, offers a comprehensive collection of average annual wage data spanning from the year 2000 to 2022. This dataset is a valuable resource for researchers, analysts, economists, and data enthusiasts interested in understanding the economic trends and wage dynamics across various countries over the past two decades.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Connecticut. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Connecticut, the median income for all workers aged 15 years and older, regardless of work hours, was $56,279 for males and $38,121 for females.

These income figures highlight a substantial gender-based income gap in Connecticut. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the state of Connecticut.

- Full-time workers, aged 15 years and older: In Connecticut, among full-time, year-round workers aged 15 years and older, males earned a median income of $82,816, while females earned $68,358, leading to a 17% gender pay gap among full-time workers. This illustrates that women earn 83 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Connecticut.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Connecticut median household income by race. You can refer the same here

This dataset provides quarterly personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis . Data includes the following estimates: personal income, per capita personal income, proprietors' income, farm proprietors' income, compensation of employees and private nonfarm earnings, compensation, and wages and salaries for wholesale trade. Personal income, proprietors' income, and farm proprietors' income available beginning 1997; per capita personal income available beginning 2010; and all other data beginning 1998.

Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s midquarter population estimates.

Proprietors' income is the current-production income (including income in kind) of sole proprietorships, partnerships, and tax-exempt cooperatives. Corporate directors' fees are included in proprietors' income. Proprietors' income includes the interest income received by financial partnerships and the net rental real estate income of those partnerships primarily engaged in the real estate business.

Farm proprietors’ income as measured for personal income reflects returns from current production; it does not measure current cash flows. Sales out of inventories are included in current gross farm income, but they are excluded from net farm income because they represent income from a previous year’s production.

Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee.

Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government.

Private nonfarm wages and salaries is wages and salaries excluding farm and government. Wages and salaries is the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans.

More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

Canaria’s Glassdoor Salary Data provides unparalleled, company-specific, real-time salary insights across a wide range of industries in the USA. Our dataset offers verified base and additional pay information, sourced directly from Glassdoor, ensuring accuracy and relevance. Whether you are analyzing payroll structures or benchmarking salaries, our Glassdoor Salary Data is designed to meet the high standards required for strategic decision-making in compensation planning.

What Makes Our Salary Data Unique? Our salary data stands out due to its comprehensive coverage and the real-time nature of the insights. Sourced from verified employee submissions on Glassdoor, each salary report is cross-checked to ensure reliability. This gives you the most accurate salary data, including both base pay and additional earnings such as bonuses, stock options, and other compensatory benefits. With Glassdoor being one of the most trusted platforms for employee feedback, the data’s integrity is unmatched.

Additionally, our salary data can be seamlessly integrated with Canaria’s Job Postings Data and Company Data Products, allowing you to combine detailed compensation insights with company and job market analytics. This interconnected ecosystem gives businesses a 360-degree view of workforce trends, making it easier to link salary information to company-specific performance and job availability.

Sourcing Methodology Canaria’s Glassdoor Salary Data is sourced from employee-reported information on Glassdoor. We ensure every data point is verified for accuracy and relevance. The dataset includes key compensation metrics such as total pay, base salary, bonuses, and additional compensation. This allows for a deep dive into salary structures, helping you get a full understanding of the total remuneration packages offered across various industries. Moreover, the data is updated in real-time, ensuring that you always have access to the latest salary trends.

Primary Use-Cases and Industry Applications Our salary data serves multiple use cases across a range of verticals:

Payroll Benchmarking: Leverage Glassdoor salary data to compare your company’s compensation against industry standards. Talent Acquisition: Recruiters can use our data to structure competitive job offers based on real-time, verified salary insights from Glassdoor. Investment Analysis: Financial analysts can use salary data to evaluate labor costs and make informed decisions about company profitability. Compensation Strategy: HR professionals can develop market-competitive compensation plans using verified Glassdoor data for both base and additional pay. Fit Into Our Broader Data Offering Canaria’s Glassdoor Salary Data is just one part of our broader data product suite. By joining this salary data with our Job Postings Data and Company Data Products, you can unlock more value and insights. For example, linking salary insights with job postings helps recruiters and HR professionals identify competitive salaries for specific job titles and locations. Additionally, combining company-specific salary data with broader company performance metrics allows for a detailed understanding of compensation trends within specific industries or regions.

Our data ecosystem provides a complete package for salary benchmarking, job market analytics, and company evaluation, making it a critical tool for businesses aiming to stay competitive in today’s job market.

With a focus on real-time updates, verified employee data, and comprehensive coverage of both base and additional pay, Canaria’s Glassdoor Salary Data is the trusted solution for businesses seeking granular, actionable salary insights.

Graph and download economic data for Total Wages and Salaries in Louisiana (LAWTOT) from Q1 1998 to Q1 2025 about LA, salaries, wages, and USA.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in New York County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 25,551(3.30%) households where the householder is under 25 years old, 304,498(39.27%) households with a householder aged between 25 and 44 years, 241,081(31.09%) households with a householder aged between 45 and 64 years, and 204,246(26.34%) households where the householder is over 65 years old.
• In New York County, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New York County median household income by age. You can refer the same here