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

# The need for interpolation
If a reading is missing between two consecutive readings, one approach is to replace the missing value using interpolation over the values that are present. 

The signal in a weakly linked circuit is a good real-world example of a noisy signal. It is easy to identify the shape of the signal by looking at it, but difficult to quantify its mathematical form. This causes a lot of frustration amongst engineers, and that is why interpolation is a necessary tool to have.

The `interp1d()` function from the `scipy.interpolate` sub-package interpolates the data based on splines of varying order. `interp1d` returns an object which behaves like a function. When this function is given an arbitrary value of $x$, it returns the corresponding interpolated $y$ value. 

This is a very useful technique when plotting data. Let's see a code example of this along with the graphs below:

In this lesson, we will learn about the need for interpolation and how to implement it. 

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Interpolation

The need for interpolation