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Salary Benchmarks for Data Roles
Data Science and Analytics
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About
The factors that influence data analyst salaries at major technology companies. It aims to provide insights into how education, skills, and experience impact earning potential in the tech industry. The data covers various roles, including data scientists and data engineers, at companies like IBM, Google, and Amazon, reflecting the high demand for professionals who can use data to drive business innovation. It's designed for those looking to understand compensation trends and the key attributes that top tech firms seek in data professionals.
Columns
- work_year: The year the salary was paid (ranging from 2020 to 2023).
- experience_level: The level of experience on the job during the year (e.g., SE for Senior-level/Expert, MI for Mid-level/Intermediate).
- employment_type: The type of employment for the role (e.g., FT for Full-time).
- job_title: The specific role held during the year (e.g., Data Engineer, Data Scientist).
- salary: The gross salary figure in the original currency.
- salary_currency: The currency of the salary paid (e.g., USD, EUR).
- salary_in_usd: The gross salary converted to US Dollars (USD).
- employee_residence: The employee's primary country of residence during the work year.
- remote_ratio: The overall amount of work done remotely (0 for no remote work, 50 for partially remote, 100 for fully remote).
- company_location: The country where the company's main office is located.
- company_size: The size of the company based on the number of employees (e.g., M for Medium-sized).
Distribution
The dataset is provided as a single CSV file named
ds_salaries new.csv, with a size of approximately 213.83 kB. It contains 3,755 records across 11 columns. The data is structured with no missing or mismatched values.Usage
This dataset is ideal for analysing salary trends in the data science and analytics fields over time. It can be used to build predictive models for salaries based on factors like experience level, job title, company size, and location. Human resources professionals can use it for benchmarking compensation packages, while aspiring data analysts can use it to understand career and salary progression.
Coverage
The data covers a time range from 2020 to 2023. Geographically, it has a strong focus on the United States, with 80% of employees residing there and 81% of companies located there. The United Kingdom is the next most represented country. The dataset includes various job titles, with Data Engineer and Data Scientist being the most common, and primarily covers full-time employment at medium-sized companies.
License
CC0: Public Domain
Who Can Use It
- Aspiring Data Scientists: To research potential salaries and career paths.
- HR and Recruitment Professionals: For salary benchmarking and talent acquisition strategies.
- Academic Researchers: To study employment and compensation trends in the tech industry.
- Data Analysts: For portfolio projects focused on exploratory data analysis and predictive modelling.
Dataset Name Suggestions
- Tech Data Professional Salary Trends 2020-2023
- Global Data Science & Analytics Salaries
- Data Job Compensation Insights
- Salary Benchmarks for Data Roles
Attributes
Original Data Source:Salary Benchmarks for Data Roles
QUALITY