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The module “Python for Computational Science” is a full semester lecture course
condensed into 2 weeks (10 work days).
Each day is roughly split as follows:
- About 2 hours of taught material (with short break) delivered in lecture
- Self-paced problem solving: students attempt programming exercises. Feedback
on completed solutions is provided by a robot (and in addition tutor, if desired)
Schedule for each day (using German local time):
| 10:00-12:30 |
Lectures (including break) |
| 14:00-17:00 |
Tutors available for help with exercises |
On the First day (Monday, 15 January 2024), we deviate from that the schedule a
little. Anticipated plan:
| 10:00 |
Morning lecture 1 |
| 11:00 |
Lab 1: complete training1.py |
| 12:00 |
Morning lecture 2 |
| 13:00 |
Lunch break |
| 14:00-17:00 |
Tutors available for help with exercises (training2, lab2) |
We will be using Zoom, and the links will be shared with you.
and others (to be confirmed).
Prediction of topic distribution. (May change a little depending on discussions during lectures).
| Mon |
lecture 01 |
python, ipython, variables, basic data types, spyder, functions, |
| Mon |
lecture 02 |
style (pep8), bools, if-then, import modules |
| Tue |
lecture 03 |
sequences (lists, tuples, strings), loops, range |
| Wed |
lecture 04 |
variables, comparison and identify, reading and writing text files, string processing, |
| Thu |
lecture 05 |
exceptions, print, higher order functions |
| Fri |
lecture 06 |
modules, name spaces, default values and keyword argument, list comprehension, dictionaries |
| Mon |
lecture 07 |
recursion, root finding, computing derivatives, numpy |
| Tue |
lecture 08 |
higher order functions II, matplotlib, Jupyter, numerical integration |
| Wed |
lecture 09 |
closures, scipy, interpolation, curve fitting, optimisation, pip |
| Thu |
lecture 10 |
ODEs, sympy, testing, Object Orientation |
| Fri |
lecture 11 |
pandas, outlook |
There are no credit points or certificates available for (the voluntary) participation in this course.
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