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.

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Examine the Dataset

Examine the Dataset