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.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Network systems and data communications analysts occupations: 16 years and over: Men (LEU0254637800A) from 2000 to 2010 about analysts, communication, second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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 ---

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Market research analysts and marketing specialists occupations: 16 years and over was 1659.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Market research analysts and marketing specialists occupations: 16 years and over reached a record high of 1659.00000 in January of 2024 and a record low of 1135.00000 in January of 2014. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Market research analysts and marketing specialists occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.

Analysis of ‘US Minimum Wage by State from 1968 to 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017 on 12 November 2021.

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

US Minimum Wage by State from 1968 to 2020

The Basics

• What is this? In the United States, states and the federal government set minimum hourly pay ("minimum wage") that workers can receive to ensure that citizens experience a minimum quality of life. This dataset provides the minimum wage data set by each state and the federal government from 1968 to 2020.

• Why did you put this together? While looking online for a clean dataset for minimum wage data by state, I was having trouble finding one. I decided to create one myself and provide it to the community.

• Who do we thank for this data? The United States Department of Labor compiles a table of this data on their website. I took the time to clean it up and provide it here for you. :) The GitHub repository (with R Code for the cleaning process) can be found here!

Content

This is a cleaned dataset of US state and federal minimum wages from 1968 to 2020 (including 2020 equivalency values). The data was scraped from the United States Department of Labor's table of minimum wage by state.

Description of Data

The values in the dataset are as follows: - Year: The year of the data. All minimum wage values are as of January 1 except 1968 and 1969, which are as of February 1. - State: The state or territory of the data. - State.Minimum.Wage: The actual State's minimum wage on January 1 of Year. - State.Minimum.Wage.2020.Dollars: The State.Minimum.Wage in 2020 dollars. - Federal.Minimum.Wage: The federal minimum wage on January 1 of Year. - Federal.Minimum.Wage.2020.Dollars: The Federal.Minimum.Wage in 2020 dollars. - Effective.Minimum.Wage: The minimum wage that is enforced in State on January 1 of Year. Because the federal minimum wage takes effect if the State's minimum wage is lower than the federal minimum wage, this is the higher of the two. - Effective.Minimum.Wage.2020.Dollars: The Effective.Minimum.Wage in 2020 dollars. - CPI.Average: The average value of the Consumer Price Index in Year. When I pulled the data from the Bureau of Labor Statistics, I selected the dataset with "all items in U.S. city average, all urban consumers, not seasonally adjusted". - Department.Of.Labor.Uncleaned.Data: The unclean, scraped value from the Department of Labor's website. - Department.Of.Labor.Cleaned.Low.Value: The State's lowest enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.High.Value are identical. (Some states enforce different minimum wage laws depending on the size of the business. In states where this is the case, generally, smaller businesses have slightly lower minimum wage requirements.) - Department.Of.Labor.Cleaned.Low.Value.2020.Dollars: The Department.Of.Labor.Cleaned.Low.Value in 2020 dollars. - Department.Of.Labor.Cleaned.High.Value: The State's higher enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.Low.Value are identical. - Department.Of.Labor.Cleaned.High.Value.2020.Dollars: The Department.Of.Labor.Cleaned.High.Value in 2020 dollars. - Footnote: The footnote provided on the Department of Labor's website. See more below.

Data Footnotes

As laws differ significantly from territory to territory, especially relating to whom is protected by minimum wage laws, the following footnotes are located throughout the data in Footnote to add more context to the minimum wage. The original footnotes can be found here.

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

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management analysts occupations: 16 years and over: Women (LEU0254742300A) from 2000 to 2024 about analysts, management, second quartile, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Women was 1518.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Women reached a record high of 1553.00000 in January of 2022 and a record low of 1144.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

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.

The worldwide full-time employment in the ICT sector is projected to reach 55.3 million in 2020 (pre-corona estimation), an increase of 3.9 percent over 2019. Software developer/engineer, user support specialist and systems analyst are three major job roles in the ICT industry.

IT executives are the highest paid    

The average salary for IT professionals ranges from tens of thousands of U.S. dollars to over a hundred thousand, depending on the region. Those working in North America are at the higher end of the salary spectrum compared to IT professionals in Latin America. Unsurprisingly, IT professionals who work in executive positions are the most paid in the global IT industry, followed by those who work in cloud computing, a technology that is being rapidly adopted in today’s information world. Apart from working towards a lucrative position, IT professionals can also obtain certain certifications that improve their value in the eyes of employers and recruiters. Google’s Professional Cloud Architect certification, for example, is the highest –paying certification among IT professionals worldwide in 2020.

A growing demand for IT professionals   

The IT industry consists of a vast range of fields related to computers and digital information. IT professionals have become some of the most in-demand members of today’s labor force, as leading companies across industries continue to push for widespread adoptions of digital technologies. IT sector employment includes professionals from various sub-fields, such as technology manufacturing, telecommunications and internet services, software publishing, and many others.

Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers

At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.

By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.

What Makes Lucror’s Fixed Income Data Unique?

Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.

Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.

Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.

How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:

• Public Sources: Includes issuer filings, bond prospectuses, financial reports, and market data.
• Proprietary Analysis: Leveraging proprietary models, our team enriches raw data to provide actionable insights.
• Expert Review: Data is validated and adjusted by experienced analysts to ensure accuracy and relevance.
• Regular Updates: Models are continuously updated to reflect market movements, regulatory changes, and issuer-specific developments.

This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.

Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.

1. Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.

2. Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.

3. Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.

Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.

Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.

By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...

Graph and download economic data for Employed full time: Wage and salary workers: Financial analysts occupations: 16 years and over: Men (LEU0254582800A) from 2000 to 2019 about analysts, occupation, full-time, males, salaries, workers, financial, 16 years +, wages, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Information security analysts occupations: 16 years and over was 2424.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Information security analysts occupations: 16 years and over reached a record high of 2424.00000 in January of 2024 and a record low of 1389.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Information security analysts occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Financial analysts occupations: 16 years and over: Women was 1386.00000 $ in January of 2019, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Financial analysts occupations: 16 years and over: Women reached a record high of 1386.00000 in January of 2019 and a record low of 756.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Financial analysts occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management analysts occupations: 16 years and over was 1937.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management analysts occupations: 16 years and over reached a record high of 1937.00000 in January of 2024 and a record low of 1017.00000 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management analysts occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Men was 1894.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Men reached a record high of 1894.00000 in January of 2023 and a record low of 1410.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer systems analysts occupations: 16 years and over: Men - last updated from the United States Federal Reserve on June of 2025.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Operations research analysts occupations: 16 years and over: Women was 1481.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Operations research analysts occupations: 16 years and over: Women reached a record high of 1665.00000 in January of 2023 and a record low of 810.00000 in January of 2002. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Operations research analysts occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Financial analysts occupations: 16 years and over: Men (LEU0254636200A) from 2000 to 2019 about analysts, second quartile, occupation, full-time, males, salaries, workers, earnings, financial, 16 years +, wages, median, employment, and USA.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

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

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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

Graph and download economic data for Employed full time: Wage and salary workers: Computer systems analysts occupations: 16 years and over: Women (LEU0257860600A) from 2011 to 2024 about analysts, computers, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Analysis of ‘Strategic Measure_Median Family Income’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ac3b4504-dfca-46e4-991f-49aad065559a on 26 January 2022.

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

This dataset provides information about the median family income in Austin (SD23 measure EOA.B.6). Median family income (MFI) is calculated annually by the U.S. Department of Housing and Urban Development (HUD) to help set income limits that determine eligibility for assisted housing programs. This dataset includes the MFI calculated by HUD for the Austin-Round Rock MSA and the United States. For years prior to FY 2016, HUD reported on the Austin-Round Rock-San Marcos MSA instead of the Austin-Round Rock MSA.

This information is important in understanding economic and affordability trends in Austin. Additionally, the Housing and Planning Department uses median family income data in setting income limits for affordable housing programs in Austin.

View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Median-Family-Income/r93e-edn2

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