Summary  
This chapter demonstrates printing the first rows of a dataset and matching each column with its statistical data type.

General domain of usage  
IT salary 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.

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

Examine the Dataset

Awesome!

Examine the Dataset