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

## Representing data through percentiles
Another useful description of a dataset is by using percentiles. 

For this we consider ordered data, meaning data that is sorted in ascending order. The $25^{th}$ percentile marks a data point in the ordered data such that $25\%$ of the data is below this data point and thus $75\%$ is above this data point. If we say that the $25^{th}$ percentile score on an exam was 85%, then $25\%$ of the candidates scored less than $85\%$ on the exam. 

The percentiles of a dataset are commonly referred to as the 'empirical percentiles' as they are the percentiles of the dataset, not of the underlying distribution. The $50^{th}$ empirical percentile is equivalent to the median of the data. Common intervals to look at are the $50\%$ region around the median, also called the **interquartile range** or IQR. 

IQR runs from the $25^{th}$ empirical percentile to the $75^{th}$ empirical percentile.  The $95\%$ region, which runs from the $2.5^{th}$ empirical percentile to the $97.5^{th}$ empirical percentile. Percentiles of a dataset may be computed with the `percentile()` function in the `numpy` package. The first argument is the data, the second argument is a list of percentiles:

# Representing data through percentiles
Another useful description of a dataset is by using percentiles. 

For this we consider ordered data, meaning data that is sorted in ascending order. The $25^{th}$ percentile marks a data point in the ordered data such that $25\%$ of the data is below this data point and thus $75\%$ is above this data point. If we say that the $25^{th}$ percentile score on an exam was 85%, then $25\%$ of the candidates scored less than $85\%$ on the exam. 

The percentiles of a dataset are commonly referred to as the 'empirical percentiles' as they are the percentiles of the dataset, not of the underlying distribution. The $50^{th}$ empirical percentile is equivalent to the median of the data. Common intervals to look at are the $50\%$ region around the median, also called the **interquartile range** or IQR. 

IQR runs from the $25^{th}$ empirical percentile to the $75^{th}$ empirical percentile.  The $95\%$ region, which runs from the $2.5^{th}$ empirical percentile to the $97.5^{th}$ empirical percentile. Percentiles of a dataset may be computed with the `percentile()` function in the `numpy` package. The first argument is the data, the second argument is a list of percentiles:

In this lesson, we will learn about percentiles.

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Percentiles

Representing data through percentiles