Welcome back! This lesson is all about descriptive statistics and understanding the various characteristics of the Titanic dataset.

So, why do we need to study statistics when dealing with data? Well, statistics is a branch of mathematics dealing with data collection, organization, and interpretation. In data science, we use statistics to extract meaningful insights and knowledge from data.

Statistics helps us deal with the data's complexity by reducing a complex dataset into a simpler summary. It assists in the presentation and visualization of the data, thereby making our data analysis or machine learning model more precise.

Take our current dataset, for instance, which comprises various demographics and passenger information; wouldn't it be interesting to know the average age or to gauge the variety in travelers' fares? Our lesson will focus on extracting these primary statistical features from our dataset, helping us better comprehend the Titanic voyage.

Descriptive statistics summarise and organize the characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In pandas, there's a function called describe(), which calculates the basic statistics for all continuous variables, i.e., types of variables that can take on an infinite number of values within a specific range. It provides the count, mean, standard deviation (std), min, quartiles, and max in its output.

Firstly, let's import the libraries we will be using and load the dataset:

The output of the head command will be like this: