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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

Python’s 2-D and 3-D plotting library Matplotlib produces high-quality figures in a variety of formats and interactive environments. Using this library, you can generate simple plots, bar charts, scatter plots, contour plots, error charts, etc. Matplotlib makes these things a lot easier because it is easy to get started with. It has support for LaTeX formatted labels and texts.

What makes Matplotlib highly suitable for generating figures for scientific publications, is that all aspects of the figure can be controlled programmatically. This is important for reproducibility and is convenient when one needs to regenerate the figure with updated data or change its appearance. 

The submodule `matplotlib.pyplot` provides an object-oriented interface for the plotting library.


In this lesson, we will discuss the basic use of plot command and how to save figures.

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Basic Plotting