Computer course - Python 3 7404-FIZ-KKP

https://sites.google.com/view/python3-umk (term 2025/26)

The aim of the course is to introduce all the tools necessary to design, develop,
and maintain a Python-based software, from code prototyping in interactive
notebooks, git version control, interfacing Python code with C/C++ using
Pybind 11, preparing Python tests, setting up automatic workflows/pipelines to
check new commits, creating and maintaining a manual with Sphynx, to testing
and optimizing code.

Term 2025/26:

The aim of the course is to introduce all the tools necessary to design, develop, and maintain a Python-based software, from code prototyping in interactive notebooks, git version control, interfacing Python code with C/C++ using Pybind 11, preparing Python tests, setting up automatic workflows/pipelines to check new commits, creating and maintaining a manual with Sphynx, to testing and optimizing code.

The course will consist of 15 computer laboratories. The preliminary schedule is:
20 Oct. 11:00-13:00 Introduction to code prototyping with Jupyter notebooks
27 Oct. 11:00-13:00 Introduction to Git version control
03 Nov. 11:00-13:00 How to structure a project
13 Nov. 08:00-10:00 Define your Python project
17 Nov. 1100-13:00 Git version control, part 2
20 Nov. 08.00-10:00 Package and Distribution management
24. Nov. 11:00-13:00 Code formatting best practices
27 Nov. 08:00-10:00 Python tests
01 Dec. 11:00-13:00 Pybind11: interfacing C/C++ and Python
08 Dec. 11:00-13:00 Building the project
15 Dec. 11:00-13:00 Setting up git pipelines and workflows
07 Jan. 11:00-13:00 Project work
22 Jan. 08:00-10:00 Creating a manual for your project
26 Jan. 11:00-13:00 Introduction to performance testing and code optimization
02 Feb. 11:00-13:00 Project presentations, final summary of the course

Total student workload

Total student workload: 60 hours - Computer labs: 30 hours - Individual project work: 30 hours

Learning outcomes - knowledge

- fundamental knowledge of scientific programming in Python, - advanced object-oriented programming, - best practices to design and maintain a Python project, - basic knowledge on interfacing Python and C/C++ code using Pybind11, - fundamentals of Git version control.

Learning outcomes - skills

- interactive code prototyping with Jupyter notebooks, - practical design and maintenance of a Python project using Git version control, - advanced Python programming skills, - building and packaging Python-based projects, - code testing and optimization.

Learning outcomes - social competencies

- oral presentation and group work.

Teaching methods

Lectures combined with computer laboratory focused on project-based tasks.

Expository teaching methods

- problem-based lecture

Exploratory teaching methods

- laboratory
- project work

Prerequisites

Prior participation in 1-2 basic Python courses, or basic to intermediate knowledge of Python programming.

Course coordinators

Term 2022/23Z:

Katharina Boguslawski

Term 2024/25:

Grzegorz Kowzan

Term 2025/26:

Iulia-Emilia Brumboiu

Learning outcomes

Learning outcomes - knowledge:
1. scientific programming in Python using numpy and scipy
2. interfacing to C++ code using pybind11
3. software development in python
4. version control using git

Realises effects: K_W03, K_W05, K_W07 for FT and K_W01, K_W10 for IS
Learning outcomes - skills:
1. advanced object programming
2. design and maintenance of your own python package
3. solving physical problems using python
4. unit tests in python

Realises effects: K_U06 for FT and K_U01, KU_09, KU_10, KU_13, KU_22 for IS
Learning outcomes - social competencies: None
Teaching methods: Computer lab - teaching issues being simultaneously programmed by students under supervision
Exploratory teaching methods: - laboratory
- practical
1. Design and maintenance of a python package using version control systems
2. Problem solving using numpy / scipy applicable to the large scale

Assessment criteria

The final grade will be a weighted average of grades for (1) the active
participation in classes (30%) and (2) the evaluation of the final software
package (70%):

0% - 49% - grade: 2
50% - 60% - grade: 3
61% - 70% - grade: 3+
71% - 80% - grade: 4
81% - 90% - grade: 4+
91% - 100% - grade: 5

Bibliography

Project design in Python: https://docs.python-guide.org/writing/structure/
Git commits: https://www.conventionalcommits.org/en/v1.0.0/
Style guide for Python code: https://peps.python.org/pep-0008/

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 7404-FIZ-KKP in USOSweb