Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

work_year The year the salary was paid.

experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

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.

Dataset

This dataset was created by Prithviraj

Contents

1) Data Introduction • The Data Science Salaries 2023 Dataset is a global annual salary analysis dataset that summarizes a variety of information in a tabular format, including salary, career, employment type, job, remote work rate, and company location and size for data science jobs as of 2023.

2) Data Utilization (1) Data Science Salaries 2023 Dataset has characteristics that: • Each row contains 11 key characteristics, including year, career level, employment type, job name, annual salary (local currency and USD), employee country of residence, remote work rate, company location, and company size. • Data is organized to reflect different countries, jobs, careers, and work patterns to analyze pay and work environments in data science in three dimensions. (2) Data Science Salaries 2023 Dataset can be used to: • Data Science Salary Analysis and Comparison: Analyzing salary levels and distributions by job, career, country, and company size can be used to understand industry trends and market value. • Establishing Recruitment and Career Strategies: It can be applied to recruitment strategies, career development, global talent attraction, etc. by analyzing the correlation between various working conditions and salaries such as remote work rates, employment types, and company location.

Dataset Summary

Briefly summarize the dataset, its intended use and the supported tasks. Give an overview of how and why the dataset was created. The summary should explicitly mention the languages present in the dataset (possibly in broad terms, e.g. translations between several pairs of European languages), and describe the domain, topic, or genre covered.

  Supported Tasks and Leaderboards

For each of the tasks tagged for this dataset, give a brief description of the tag… See the full description on the dataset page: https://huggingface.co/datasets/Einstellung/demo-salaries.

Dataset

This dataset was created by SRF

Contents

This dataset contains the salaries of Data Science Professionals for year 2020 and 2021.

About Dataset :

work_year : The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level : The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type : The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title : The role worked in during the year. salary The total gross salary amount paid.

salary_currency : The currency of the salary paid as an ISO 4217 currency code.

salaryinusd : The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence : Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location : The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size : The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

What's inside dataset

work_year

The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level

The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type

The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title

The role worked in during the year. salary The total gross salary amount paid.

salary_currency

The currency of the salary paid as an ISO 4217 currency code.

salary_in_usd

The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence

Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio

The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location

The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size

The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

H-1B visa sponsorship trends for Data Scientist, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

Data Science Jobs and Salaries in Karachi

Dataset

This dataset was created by Khalid Ameen

Released under Apache 2.0

Contents

Dataset

This dataset was created by Lorthar Amissah Quansah

Released under Apache 2.0

Contents

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

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

Context

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, Data Engineers in various cities across India (2022).

Acknowledgements

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

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

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

Dataset

This dataset was created by Aiebee-Iberedem Nkereuwem Tim

Released under GNU Free Documentation License 1.3

Contents

Graph and download economic data for Employed full time: Wage and salary workers: Computer scientists and systems analysts occupations: 16 years and over (LEU0254477000A) from 2000 to 2010 about analysts, computers, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Life, physical, and social science occupations: 16 years and over: Men (LEU0254587200A) from 2000 to 2024 about science, life, occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

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

Dataset de Salarios en el Ámbito de Inteligencia Artificial y Ciencia de Datos

Este conjunto de datos recopila información salarial de diversos puestos de trabajo vinculados a inteligencia artificial y a la ciencia de datos en todo el mundo. Es una adaptación del conjunto original salaries que pertenece a ai-jobs.net.

Se ha ampliado con nuevas variables para ofrecer una visión más completa de las condiciones laborales. Las variables originales son:

• work_year: Año en que se pagó el salario (Categórica)
• experience_level: Nivel de experiencia (EN: Junior, MI: Medio, SE: Senior, EX: Director/Ejecutivo) (Categórica)
• employment_type: Tipo de contrato (PT: Tiempo parcial, FT: Tiempo completo, CT: Contrato de servicio, FL: Freelance) (Categórica)
• job_title: Puesto de trabajo (Categórica)
• salary: Salario bruto anual (Cuantitativa)
• salary_currency: Divisa del salario bruto (Categórica)
• salary_in_usd: Salario en USD (Cuantitativa)
• employee_residence: País de residencia del empleado (ISO3166) (Categórica)
• remote_ratio: Cantidad de teletrabajo (0: Presencial, 50: Híbrido, 100: Remoto) (Categórica)
• company_location: País de la empresa (ISO3166) (Categórica)
• company_size: Tamaño de la empresa (S: Menos de 50 empleados, M: Entre 50 y 250 empleados, L: Más de 250 empleados) (Categórica)

Las nuevas variables incluidas son:

• company_continent: Continente donde se encuentra la empresa (Categórica)
• employee_continent: Continente donde reside el empleado (Categórica)
• company_continent_region: Región continental donde está ubicada la empresa (Categórica)
• employee_continent_region: Región continental donde reside el empleado (Categórica)
• salary_rounded_in_eur: Salario en euros redondeado (calculado con la media de cada año) (Cuantitativa)
• salary_k_in_eur: Salario en miles de euros (Cuantitativa)
• salary_type: Tipo de salario (VL: Very Low, L: Low, M: Medium, H: High, VH: Very High) (Categórica)
• same_country: ¿Residencia y lugar de trabajo en el mismo país? (Categórica)

Dataset

This dataset was created by Shubhranshu Arya

Released under MIT

Contents