Welcome to the first step in our course. This lesson demonstrates how to load and explore the Airline dataset using Python, showcasing its basic structure and notable features.

Understanding the dataset you're working with is the first key step in any data science project. Exploring the dataset helps detect trends, outliers, incorrect data, and much more. As a data scientist, it is essential to understand which questions your data can answer and which it cannot. Let's dive in and explore!

Our dataset, called the "Flights" dataset, belongs to the Seaborn library. This dataset provides a monthly tally of airline passengers from 1949 to 1960.

The Flights dataset comprises three distinct columns:

• year: Represents the year in which the count of passengers was taken.
• month: Points towards the month in which the passenger count was gathered.
• passengers: Indicates the number of passengers that traveled in that month of a particular year.

Let's load the dataset in Python. You can easily load this dataset, along with other inbuilt Seaborn datasets, using the load_dataset() method as follows: