# Scientific Computing¶

This section discusses several key aspects of scientific computing that are core to the ability to do modern economics, data science, and statistics

As the size of our data and the complexity of our models has increased, we have become increasingly reliant on computers to perform computations that we simply cannot do by hand

In this section, we will cover

- Python’s main numerical library numpy and how to work with its array type
- A basic introduction to visualizing data with matplotlib
- A refresher on some key linear algebra concepts
- A review of basic probability concepts and how to use simulation to learn about economics
- Using a computer to perform optimization

Many of the tools learned in this section will continue to show up throughout the pandas and applications sections

Note

Warning: This section has more formal math than the previous material (and there will be more math as you cover certain methods)

We expect that there is a wide diversity in the previous mathematical training of the students using this material, so, for those who have slightly less preparation, please don’t let this scare you

We have found that, although it will require some extra effort, that understanding these tools will give you a leg up in almost any career you consider in the future