I have conducted exploratory data analysis on a dataset of data science job salaries, which includes features such as job location, years of work experience, job title, company size, salary, and more. I have used various visualization techniques, including univariate, bivariate, and multivariate analysis, to uncover patterns and insights in the data. I have also utilized unique chart types and formatting to present the data in a clear and easy-to-understand manner. The goal is to have a well-explained and clear representation of data.
| 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 | 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) |