Summary  
Demonstrates inspecting a tabular dataset by printing its first observations and matching each column to its appropriate data type.

General domain of usage  
Exploratory data analysis

In this section, a sample of IT specialists' salaries will be analyzed. Begin by examining the first five observations of the dataset:

- `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 begins a review of data types in statistics, followed by an activity to match each column name with its corresponding data type.

Learn core statistical concepts used in data analysis with Python. The course covers descriptive statistics, including mean, median, mode, variance, and standard deviation, as well as sampling, probability distributions, the Central Limit Theorem, and outlier detection.

Examine the Dataset