Lectures on Scientific Computing with Python

By J. Robert Johannson’s

• Lecture-0 Scientific Computing with Python - I have a few comments.
  • First, in 2015 IPython Notebook was officially renamed Jupyter.
  • Second, I recommend Anaconda if you are going to install python and all of its packages on your own computer. I have had no problem installing and running the whole Jupyter environment on Window, MacOsX, and Linux machines. The Mac installation is even easier now than he describes in the link.
• Lecture-1 Introduction to Python Programming
  • This is a nice quick tour through the python language. Yo ushould keep this link handy as you learn python.
• Lecture-2 Numpy - multidimensional data arrays
  • The heart of data analysis usually involves arrays of data. This link is a tour of the numpy library which implements fast array math in python. You should at least scan through the first third of this page. He also shows some reading from files and plotting.
  • The last two-thirds is optional for getting started. It covers multi-dimensional array, linear algebra, and other topics.
• Lecture-3 Scipy - Library of scientific algorithms
• This lecture shows python’s ability in using advanced mathematical techniques. Some that maybe of interest to you are the Linear Algebra about halfway through the notebook, and the Statistics near the bottom.
• Lecture-4-Matplotlib
• This is an important lecture because you will definitely be doing plots. You should keep a link to this page handy because you will need many of the things on this page to make professional quality graphs and plots.

Not linked and of less importance

You can find the complete set at Johansson’s link Lecture-4 Matplotlib - 2D and 3D plotting Lecture-5 Sympy - Symbolic algebra Lecture-6A Fortran and C integration Lecture-6B HPC Lecture-7 Revision Control Software