As you know, data science is super hot. Whether you're analyzing data, working as an engineer, or creating fancy machine-learning models, your job likely involves all sorts of mathematical operations.
This sort of work requires fast, efficient processing. And thus, for years, people used the fastest-executing programming languages they could find — C, C++, and Java. In many cases, they used specialized languages and tools, such as Matlab, which did the job but cost quite a lot.
In the last few years, Python has become the #1 language in data science. This puzzles a lot of people, since Python is known for being friendly, but not for being efficient.
The key to this change is NumPy. NumPy is a library that puts a thin Python shell over C arrays. You thus get the ease of use of Python, but the efficiency (in both space and time) of C.