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

# Normal distribution
The most common probability distribution is probably the Normal distribution. Random numbers from a Normal distribution may be generated with the `normal` function in the `random` subpackage of `numpy`, where the keyword `loc` is the mean (default is 0) and `scale` is the standard deviation (default is 1). 

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$\mu$ is mean and $\sigma$ is the standard deviation

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Courtesy: <a href="https://simple.wikipedia.org/wiki/Normal_distribution#/media/File:Normal_distribution_pdf.png">Wikipedia</a>

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The mean and standard deviation of a dataset can be computed with the functions `mean` and `std` of the numpy package.

In this lesson, we will learn about normal distribution.

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Normal Continuous Random Variables

Normal distribution