How should a beginner approach learning NumPy before using it with real datasets

I’m a beginner learning NumPy and Data Science. I understand basic operations such as array creation, indexing, slicing, and simple computations.

However, when I try to follow tutorials that use NumPy with datasets (for example in scikit-learn), I get confused about how the arrays are structured and used.

For example, I see code like this:

from sklearn.datasets import load_iris data = load_iris() X = data.data y = data.target print(X.shape) print(X[0])

I understand that X is a NumPy array, but I get confused about:

How to interpret the shape (rows vs columns)

How indexing works in this context

How this connects to the NumPy basics I learned

What is the correct way to think about NumPy arrays when working with real datasets like this?