Gain insights into Python for scientific computing. Explore arrays, plotting, linear equations, and algorithms using NumPy, Matplotlib, SciPy. Delve into applying tools with practical exercises.

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Python3

If you're a scientist or an engineer interested in learning scientific computing, this is the place to start.

In this course, you'll learn to write your own useful code to perform impactful scientific computations. Along the way, your understanding will be tested with periodic quizzes and exercises.

Topics covered in this course include arrays, plotting, linear equations, symbolic computation, scientific algorithms, and random variables. You’ll also be exposed to popular Python packages like NumPy, Matplotlib, SciPy, and others. In the last part of the course, the application section will test your ability to recall and apply the tools you have studied into newly learned scientific concepts.

At the end of this course, you'll be equipped with the tools necessary for everyday scientific computation.

Python for Scientists and Engineers

## Inverse
Suppose we have to solve the equation $AX=b$ for $b$.

If vector $b$ changes, one has to call `np.linalg.solve()` over and over. Instead, if $A^{-1}$ were calculated once, matrix multiplication with $b$ could be done over and over, more efficiently, to obtain different solutions.

The inverse of a matrix may be computed with the `inv` function of the `linalg` package. 

Let's use the inverse of the matrix to obtain the solution to the problem in the first [lesson](https://www.educative.io/courses/python-for-scientists-and-engineers/building-and-solving-linear-equations). 

<center>

$AX=b$

$A^{-1}AX=A^{-1}b$

$X=A^{-1}b$

</center>

If the inverse of matrix `A` is called `Ainv`, the solution for the equation may be obtained through matrix multiplication of `Ainv` with the right-hand side.  Let’s look at an implementation of this below.

# Inverse
Suppose we have to solve the equation $AX=b$ for $b$.

If vector $b$ changes, one has to call `np.linalg.solve()` over and over. Instead, if $A^{-1}$ were calculated once, matrix multiplication with $b$ could be done over and over, more efficiently, to obtain different solutions.

The inverse of a matrix may be computed with the `inv` function of the `linalg` package. 

Let's use the inverse of the matrix to obtain the solution to the problem in the first [lesson](https://www.educative.io/courses/python-for-scientists-and-engineers/building-and-solving-linear-equations). 

<center>

$AX=b$

$A^{-1}AX=A^{-1}b$

$X=A^{-1}b$

</center>

If the inverse of matrix `A` is called `Ainv`, the solution for the equation may be obtained through matrix multiplication of `Ainv` with the right-hand side.  Let’s look at an implementation of this below.

This lesson discusses various matrix operations.

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Matrix Operations

Inverse