I am considering a position in data science. Assuming that my goal is to earn the highest salary possible, investigate which factors will bring me closer to my goal.

About Dataset “Salaries of Jobs In The Data Science Domain.” Source: https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries

1. work_year: The year the salary was paid.
2. experience_level: The experience level during the year
3. employment_type: The type of employment for the role
4. job_title: The role worked in during the year.
5. salary: The total gross salary amount paid.
6. salary_currency: ISO 4217 currency code.
7. salaryinusd: The salary in USD
8. employee_residence: ISO 3166 country code.
9. remote_ratio: The overall amount of work done remotely
10. company_location: main office or contracting branch
11. company_size: The median number of workers

As you know, the more high experience level, the more high salary, but does it change linearly or quadratically?

A. The median value seems to change linearly.

The boxplot shows that small companies pay low salaries. Large companies offer a wide range of salaries.

The graph below shows a big difference in salaries depending on the job. As 3-1, sample size for each job is too small.

Data Scientist, Data Engineer, Data Analyst is the top 3. The other jobs are related to those top 3 jobs. The ratio of “Data Scientist” is 23.6% \({143 \over 607} = 0.236\)

The map shows that the US and Russia’s average salaries are high, followed by Canada and China.

However, this dataset is biased (The 59% of company_location is the US.)

## [1] 355

## [1] 607

\[{355 \over 607} = 0.585\]