Dockerfile.tar.gz

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

# What is SciPy?
If you are a scientist, researcher, or a student doing any kind of data analysis or numeric programming, then Python is worth investing some time in, even just the basics.

Like NumPy, **SciPy** is stable, mature, and widely used. Many SciPy routines are thin wrappers around industry-standard Fortran libraries such as LAPACK, BLAS, etc. It’s not really necessary to learn SciPy as a whole. A more common approach is to get some idea of what’s in the library and then look up [documentation](https://docs.scipy.org/doc/scipy/reference/index.html) as required.

## SciPy and NumPy
SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality. 

Let's plot  $y=sin(x)$ in NumPy and in SciPy:

**NumPy**



Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Introduction

What is SciPy?

SciPy and NumPy