Updated as of Jan 2024. HR reported data from Salary.com, I tried to choose at least two cities per state, cities chosen based on capital and population count. Manually researched and input data using an Excel, sorted 10th,25th,50th,75th, and 90th percentiles. The top 7 cities do not differ within percentiles as being highest paid salaried cities. Research was sought to find out the highest-paid median salaries for data analysts who are entry-level.

Landing high-paying jobs at these top tech giants requires a fine blend of education, skills, and practical experience. You need to have a deep understanding of data and its analysis, something that can be massively boosted by applying for a degree at IU. The data science degrees we offer, ranging from a Bachelor's in Data Science to an MBA in Big Data Management, provide hands-on, applicable knowledge which these top tech leaders value

Networking, demonstrating creative problem-solving, ability to identify patterns, and showcasing a portfolio of practical projects can also go a long way in getting noticed. Remember, these companies are looking for innovative minds who can use data to drive their companies forward!

IBM, a veteran of the tech industry, has long since recognised the value of data and employs data scientists to keep them on the cutting edge of technology. The average salary of a data scientist at IBM is $155,869[3] per year, along with other incentives such as bonuses.

Google, requires data scientists to improve its user experience, advertising platform and search algorithms among other things. Data scientists at Google can expect to earn $135,287[2] annually, as per recent figures provided by Glassdoor [2023].

As one of the world's largest and most successful e-commerce corporations, Amazon has a high demand for data scientists to analyse and interpret the vast volume of data they generate. The average base salary for a Data Scientist in Amazon is $128,059[1] per year in the United States, with additional compensation like bonuses and benefits [2023].

As of 2022, the median annual salary of a data analyst in the Chinese data and artificial intelligence industry reached ** thousand yuan. According to the source, junior-level employees in the technology industry gained the most from changing their jobs. In contrast, from the middle-level upwards, the salary increases are much lower after taking a position at a new employer.

This statistic displays the average salary of data analytics firm employees across India in 2016, by technology. In that year, data analysts in India who used a combination of data science and big data earned on average 1.31 million Indian rupees per year.

This statistic displays the average salary of data analytics firm employees across India in 2016, by company size. In that year, data analysts working in start-up companies in India earned on average 1.08 million Indian rupees per year.

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.

The "**Salary Data Worldwide**" dataset provides insights into wage statistics across different countries and continents. It includes information on median, average, lowest, and highest salaries, categorized by country and continent. This dataset can be valuable for analyzing salary disparities and trends worldwide.

Columns:

country_name - The name of the country. continent_name - The name of the continent where the country is located. wage_span - The frequency of salary data (e.g., Monthly). median_salary - The median salary in the country/region. average_salary - The average salary in the country/region. lowest_salary - The lowest reported salary in the country/region. highest_salary - The highest reported salary in the country/region. This dataset is a valuable resource for conducting global salary analyses, understanding salary distributions, and making cross-country salary comparisons. It can be used by data analysts, researchers, and policymakers interested in labor market trends and salary-related insights.

Usage: This dataset is suitable for exploratory data analysis (EDA), statistical analysis, and creating data visualizations to better understand salary data trends across countries and continents.

Acknowledgments: The data sources for this dataset may vary, and it's important to cite the original sources when using the dataset for analysis or research.

This statistic displays the average salary of data analytics firm employees across India in 2016, by software skills. In that year, data analysts in India who were skilled in SAS, SPSS, R and Python earned on average 1.28 million Indian rupees per year.

Overview

Data scientist is the sexiest job in the world.

How many times have you heard that? Analytics India Annual Salary Study which aims to understand a wide range of trends in data science says that the median analytics salary in India for the year 2017 is INR 12.7 Lakhs across all experience levels and skill set.

https://i.ibb.co/7Vq9D3k/croped.jpg" alt="Data Science Image">

So given the job description and other key information can you predict the range of salary of the job posting?

What kind of factors influence the salary of a data scientist? The study also says that in the world of analytics, Mumbai is the highest paymaster at almost 13.3 Lakhs per annum, followed by Bengaluru at 12.5 Lakhs. The industry of the data scientist can also influence the salary. The Telecom industry pays the highest median salaries to its analytics professionals at 18.6 Lakhs. What are you waiting for, solve the problem by predicting how much a data scientist or analytics professional will be paid by analysing the data given?

Bonus Tip:

You can analyse the data and get key insights for your career as well. Data The dataset is based on salary and job postings in India across the internet. The train and the test data consists of attributes mentioned below.

The rows of train dataset has a rich amount of information regarding the job posting such as the name of the designation and key skills required for the job. The training data and test data comprise 19802 samples and 6601 samples each.

This is a dataset that has been collected over some time to gather relevant analytics jobs posting over the years. Features Name of the company (Encoded) Years of experience Job description Job designation Job Type Key skills Location Salary in Rupees Lakhs(To be predicted) Problem Statement Based on the given attributes and salary information, build a robust machine learning model that predicts the salary range of the salary p post.

Note: Data were taken from Machine Hack Machine learning Hackathon website.

About MachineHack MachineHack is an online platform for Machine Learning competitions, assessment and hiring. We host the toughest business problems that can now find solutions in Machine Learning & Data Science.

The average annual salary of a Data Architect in India was estimated to be over *********** Indian rupees per annum, the highest among other jobs in the Data Science sector in India. It was followed by data Scientist and Database Developer roles.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856500A) from 2011 to 2023 about second quartile, occupation, compensation, benefits, jobs, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

This statistic displays the average salary of data analytics firm employees across India in 2016, by city. In that year, data analysts working in the city of Mumbai in India earned on average 990 thousand Indian rupees per year.

TeamStation AI System Report on LATAM IT Salaries 2024 A Comprehensive Analysis of Salary Trends in Latin America’s IT Sector

Introduction The 2024 TeamStation AI Salary Report provides a comprehensive analysis of IT salary structures in 19 Latin American countries, offering scientific insights into compensation trends across various job roles, experience levels, and contract types. This report leverages 1,521 salary records collected from real hiring data, offering the most precise, non-biased compensation insights in the region​

Key Findings 1. Salary Breakdown by Country Three countries lead in IT talent representation:

🇲🇽 Mexico 🇨🇴 Colombia 🇦🇷 Argentina Brazil and Chile also emerge as key players, showcasing robust demand for high-level AI, ML, and DevOps professionals. Meanwhile, Uruguay and Costa Rica provide a cost-effective alternative for high-skilled developers​

1. Role-Based Salary Trends The six most common roles in the LATAM IT market are:

Full-Stack Developer Front-End Developer Back-End Developer App Developer DevOps Engineer Data Engineer Additionally, AI, MLOps, and Cloud Engineers are seeing increasing demand, commanding salaries up to 60% higher than other IT positions​

1. Salary Ranges by Experience Level Salaries vary significantly based on seniority and contract type:

Junior Developers: $10,000 – $30,000 per year Mid-Level Developers: $20,000 – $50,000 per year Senior Developers: $25,000 – $100,000 per year (with some AI engineers exceeding this range) Full-time contracts pay the highest salaries, while freelance engagements have lower total compensation, but can reach premium rates for niche AI/ML expertise​

1. Key Statistical Insights Average salary across all roles: $30,470.02 USD Standard deviation: $56,817.32 USD (showing large variances based on expertise and role) Minimum salary recorded: $500 USD Maximum salary recorded: $800,000 USD Salary percentiles: 25th percentile: $7,000 USD 50th percentile (median): $16,300 USD 75th percentile: $36,000 USD These figures indicate a wide salary stratification, especially for senior roles and AI-related positions​ .

2. Contract Type & Compensation Salaries vary based on contract type:

Full-time developers earn higher base salaries with benefits. Freelancers earn lower annual salaries but some charge premium hourly rates in AI, Cloud, and DevOps. Mid and senior-level engineers prefer full-time contracts for higher pay and stability​ . Regional Salary Insights Highest-paying regions: 🇨🇱 Chile, 🇧🇷 Brazil, 🇲🇽 Mexico. Mid-range salaries: 🇨🇴 Colombia, 🇦🇷 Argentina. Cost-effective hiring: 🇺🇾 Uruguay, 🇨🇷 Costa Rica​ . Strategic Takeaways AI & MLOps engineers are the most expensive to hire in Mexico, Brazil, and Chile. Cloud, DevOps, and AI roles are seeing the fastest growth in salary demand. Best locations for cost-effective hiring: Colombia, Argentina, Uruguay. AI-driven hiring platforms like TeamStation AI reduce time-to-hire and salary mismatches​

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over was 1252.00000 $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over reached a record high of 1252.00000 in January of 2023 and a record low of 893.00000 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: Compensation, benefits, and job analysis specialists occupations: 16 years and over - last updated from the United States Federal Reserve on November of 2025.

India's average salary is ₹31.1L CTC (₹181,167/month take-home). Updated October 2025 with real professional data.

Indonesian JobStreet Salary & Hybrid Recommendation Dataset

Overview

The Indonesian JobStreet Salary & Hybrid Recommendation Dataset is a comprehensive, machine learning–ready dataset containing aggregated salary information from JobStreet Indonesia job postings. It was developed through a data scraping and hybrid recommendation system approach to identify average salaries across various job titles, companies, and regions in Indonesia.

This dataset is ideal for salary prediction, labor market analytics, career recommendation systems, and data-driven HR insights.

Dataset Information

• Total Records: 32,976 job postings
• Time Period: JobStreet Indonesia scraping dataset (2024)
• Geographic Coverage: Indonesia (606 unique locations)
• File Format: CSV
• File Size: ~7.8 MB
• Missing Values: None (100% complete dataset)
• Duplicates: Some overlapping listings between companies and job categories
• Target Variable: Gaji_Rata2 (Average monthly salary in IDR)

Key Features

💼 Job Classification

• Unique Job Titles: 8,686
• Unique Companies: 4,969
• Unique Locations: 606
• Average Salary (Gaji_Rata2): IDR 7.24 million/month
• Salary Range: From entry-level to executive-level roles
• Data Source: JobStreet Indonesia (public postings)

⚙️ Data Preparation

• Job data scraped from JobStreet Indonesia job listings in 1 Month on 2024
• Salaries standardized and averaged across identical job titles
• Aggregation and smoothing performed using hybrid recommender modeling (content-based + collaborative filtering)

Feature Description

Data Quality Assessment

• ✅ Zero missing values — dataset is 100% complete
• ✅ Structured and cleaned schema (uniform columns)
• ⚠️ Some duplicates may occur due to overlapping job postings
• 📊 High diversity across job types and cities
• 🇮🇩 Fully localized — all data from JobStreet Indonesia only

Machine Learning Applications

• Salary Prediction: Predict average salary by job title, company, or region
• Career Recommender Systems: Build hybrid models to suggest similar or higher-paying jobs
• Market Analytics: Analyze salary trends by location or sector
• NLP Job Classification: Cluster similar job roles using semantic text embeddings
• HR Decision Support: Compare salary averages across industries

Citation

Original Source: JobStreet Indonesia (public job listings)

License: CC BY 4.0 (Attribution required)

Version: 1.0 (2024)

About this dataset

Context

The dataset tabulates the Missouri household income by age. The dataset can be utilized to understand the age-based income distribution of Missouri income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Missouri annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Missouri household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Missouri income distribution by age. You can refer the same here

Context Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is a very good career with tremendous opportunities for advancement in the future. Already, demand is high, salaries are competitive, and the perks are numerous – which is why Data Scientist has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor. The average salary for a data scientist is Rs. 698,412 per year. With less than a year of experience, an entry-level data scientist can make approximately 500,000 per year. Data scientists with 1 to 4 years of experience may expect to earn about 610,811 per year.

Below is a dataset about Salaries provided by DataScience companies in INR. The data is scraped from Glassdoor.

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To analyze the salaries of company employees using Pandas, NumPy, and other tools, you can structure the analysis process into several steps:

Case Study: Employee Salary Analysis In this case study, we aim to analyze the salaries of employees across different departments and levels within a company. Our goal is to uncover key patterns, identify outliers, and provide insights that can support decisions related to compensation and workforce management.

Step 1: Data Collection and Preparation Data Sources: The dataset typically includes employee ID, name, department, position, years of experience, salary, and additional compensation (bonuses, stock options, etc.). Data Cleaning: We use Pandas to handle missing or incomplete data, remove duplicates, and standardize formats. Example: df.dropna() to handle missing salary information, and df.drop_duplicates() to eliminate duplicate entries. Step 2: Data Exploration and Descriptive Statistics Exploratory Data Analysis (EDA): Using Pandas to calculate basic statistics such as mean, median, mode, and standard deviation for employee salaries. Example: df['salary'].describe() provides an overview of the distribution of salaries. Data Visualization: Leveraging tools like Matplotlib or Seaborn for visualizing salary distributions, box plots to detect outliers, and bar charts for department-wise salary breakdowns. Example: sns.boxplot(x='department', y='salary', data=df) provides a visual representation of salary variations by department. Step 3: Analysis Using NumPy Calculating Salary Ranges: NumPy can be used to calculate the range, variance, and percentiles of salary data to identify the spread and skewness of the salary distribution. Example: np.percentile(df['salary'], [25, 50, 75]) helps identify salary quartiles. Correlation Analysis: Identify the relationship between variables such as experience and salary using NumPy to compute correlation coefficients. Example: np.corrcoef(df['years_of_experience'], df['salary']) reveals if experience is a significant factor in salary determination. Step 4: Grouping and Aggregation Salary by Department and Position: Using Pandas' groupby function, we can summarize salary information for different departments and job titles to identify trends or inequalities. Example: df.groupby('department')['salary'].mean() calculates the average salary per department. Step 5: Salary Forecasting (Optional) Predictive Analysis: Using tools such as Scikit-learn, we could build a regression model to predict future salary increases based on factors like experience, education level, and performance ratings. Step 6: Insights and Recommendations Outlier Identification: Detect any employees earning significantly more or less than the average, which could signal inequities or high performers. Salary Discrepancies: Highlight any salary discrepancies between departments or gender that may require further investigation. Compensation Planning: Based on the analysis, suggest potential changes to the salary structure or bonus allocations to ensure fair compensation across the organization. Tools Used: Pandas: For data manipulation, grouping, and descriptive analysis. NumPy: For numerical operations such as percentiles and correlations. Matplotlib/Seaborn: For data visualization to highlight key patterns and trends. Scikit-learn (Optional): For building predictive models if salary forecasting is included in the analysis. This approach ensures a comprehensive analysis of employee salaries, providing actionable insights for human resource planning and compensation strategy.