Course Content
Learning Statistics with Python
Learning Statistics with Python
Examine the Dataset
In this section, we will analyze a sample of IT specialists' salaries. Let's first take a look at the first five observations of the dataset:
work_year | experience_level | job_title | salary | salary_currency | salary_in_usd | company_location | company_size | |
0 | 2020 | MI | Data Scientist | 70000 | EUR | 79833 | DE | L |
1 | 2020 | SE | Machine Learning Scientist | 260000 | USD | 260000 | JP | S |
2 | 2020 | SE | Big Data Engineer | 85000 | GBP | 109024 | GB | M |
3 | 2020 | MI | Product Data Analyst | 20000 | USD | 20000 | HN | S |
4 | 2020 | SE | Machine Learning Engineer | 150000 | USD | 150000 | US | L |
work_year
- this year, the salary was paid;experience_level
- the experience level: EN is Entery-level, MI is Mid-level, SE-Senior-level, EX is Executive level;job_title
- the name of a job;salary
- the value of the salary;salary_currency
- the currency of the salary;salary_in_usd
- the value of the salary in USD;company_location
- the location of the company;company_size
- the size of the company: S-Small, M-Medium, L-Large.
Now, let's review the data types in statistics, and afterward, you'll match each column name with its respective type.
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