Summary  
This chapter demonstrates how to detect missing values in a dataset by using functions that return boolean indicators of NaN entries.  

General domain of usage  
Data cleaning

I'm happy to see you in the last section of the course. Here, you will process data on the passengers of the Titanic. First, let's examine it:

The first step of our learning is finding missing values. By the way, sometimes it is difficult or even impossible to fill all the values of the column; some of them may be missing. Such cases can spoil your result. In the dataset, they always look like this: `NaN`. First, let's find out if your data set contains missing values.

Pandas has two functions you can apply to the dataset to find missing values. Both of them will put `False` if the dataset values aren't missing, and `True` otherwise.

```python
data.isna()
# Or
data.isnull()
```


Please select the INCORRECT ways of checking for missing values.

Checking Missing