CME 193 - Introduction to Scientific Python

Course description

This short course runs for the first eight weeks of the quarter and is offered each quarter during the academic year. It is recommended for students who want to use Python in math, science, or engineering courses and for students who want to learn the basics of Python programming, and learn about relevant applications.

The goal of the short course is to familiarize students with Python’s tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. Topics covered include control flow, basic data structures, File IO, and an introduction to NumPy and SciPy.

Course information

CME 193 - Introduction to Scientific Python - Spring 2015

  • Location: 380-380Y

  • Times: Thursdays 3:45 PM - 5:35 PM for 8 weeks

Instructor: Sven Schmit (schmit at stanford dot edu)

Office hours: by appointment and directly after class.

We will be using Piazza for course communication.

Course prereqs

There are no formal prerequisites. However, past experience has shown that people with no programming experience will struggle with the pace and have to put in a lot of hours, as if it were a three unit course. Hence, some prior programming experience in some language is strongly encouraged.

If you have no programming experience, and you still want to take to course, please consider completing an introduction of Python on Codeacademy and/or Udacity before class starts. This should get you up to speed!

Course Policy

The course consists of 8 lectures. Roughly the first half is a traditional lecture, and after a short minute break, we will spend the rest of the time working on exercises in class. Hence, please bring a laptop.

Exercises will be assigned each class. There is no graded homework. At the end of the course, you should submit a portfolio demonstrating that you did the exercises assigned and a final project. For more information on the portfolio and the project, see the corresponding pages. Both are due precisely one week after the last lecture, on May 28 at noon.

To get credit, you are judged on the combination of the portfolio and project. In particular, an outstanding project can make up for a somewhat lacking portfolio, and if your project just does not work out as expected, a great portfolio can make up for that. However, in general both should be at a satisfactory level.

You are encouraged to take the class for credit, as it forces you to practice with Python and that's the best way to learn. However, anyone is also welcome to audit the class.

Honor code

You are strongly encouraged to work together and discuss both exercises and the project. However, the portfolio and project should be your own work and all the code you submit should be written by yourself. Of course, you are free, and encouraged, to search online to solutions to problems you run into (though not full solutions to the exercises), and use those. A simple example would be to look up the syntax of a for loop.