If you have Python and PIP already installed on a system, then installation of SciPy is very easy. Install it using this command:

As SciPy is more focused on scientific implementations, it provides many built-in scientific constants. These constants can be helpful when you are working with Data Science.

Optimizers are a set of procedures defined in SciPy that either find the minimum value of a function, or the root of an equation.

Sparse data is data that has mostly unused elements (elements that don't carry any information ). It can be an array like this one: [1, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0]

Graphs are an essential data structure. SciPy provides us with the module scipy.sparse.csgraph for working with such data structures.

Spatial data refers to data that is represented in a geometric space. E.g. points on a coordinate system.

We know that NumPy provides us with methods to persist the data in readable formats for Python. But SciPy provides us with interoperability with Matlab as well. SciPy provides us with the module scipy.io, which has functions for working with Matlab arrays.

In statistics, statistical significance means that the result that was produced has a reason behind it, it was not produced randomly, or by chance.

In order to see a summary of values in an array, we can use the describe() function

Normality tests are based on the skewness and kurtosis. The normaltest() function returns p value for the null hypothesis: "x comes from a normal distribution".