An easy introduction to algebra with numpy/scipy with examples and code.

In this tutorial I want to revise some basics concepts of linear algebra, least square minimization and curve fitting which are useful tools for any scientist working his way trough data analysis in python. These tools can be applied to a big variety of problems, from linear regression to ODE (ordinary differential equation).

We will start with the basics working our way to more complicated cases using the tools provided from numpy and scipy (built on top of numpy): two popular scientific computing packages for python. In this article I am using those versions:

numpy ‘1.20.0’

scipy ‘1.6.0’

but the routines we will use are not new ones, so there should be no problems in using an older version.

By the end of this article you will be able to implement your code for solving common algebra calculation on matrices and arrays, fit your data with a custom equation and apply least square regression to your problems.

I employed these techniques in some other articles like Multiple linear regression, Linear Regression, and some advanced least square minimization — curve fit with a basic analysis of covid cases with SIR model.

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