Vorlesung im Detail

Scientific Programming with Python

011516, WS2324
Dozentinnen und Dozenten
Veranstaltungstyp (SWS)
Vorlesung (2+1)
Ort und Zeit
  • Startveranstaltung einmalig:
  • 02.11.2023, 16:00, M1011
  • Ab dem 09.11.2023 dann immer:
  • M/1011 Do 14:00 2h
Modul-Zugehörigkeit (ohne Gewähr)
  • DPL:B:-:2
  • MABA:-:4:MAT-442
  • WIMAMA:-:4:MAT-442
  • TMABA:-:4:MAT-442
  • MAMA:-:4:MAT-442
  • WIMAMA:-:4:MAT-442
  • TMAMA:-:4:MAT-442
Sprechstunde zur Veranstaltung
by agreement
ohne Angabe
Python is a high-level, general-purpose programming language that enables rapid and efficient development of applications in science and industry. In this series of lectures, we study the fundamentals of the Python programming language, discuss its specific ``pythonic`` features, become familiar with prominent scientific and data science packages such as NumPy, SciPy, Pandas, and Matplotlib, and work through practical examples. The examples include function interpolation, a finite difference version of the projectile model, numerical simulation of simple one-dimensional partial differential equations of the convection-diffusion type, and others. The goal is to experience the programming power of Python in scientific applications. As advanced topics, we briefly touch on topics such as MediaPipe, an open-source framework for building pipelines to perform computer vision inference, Mpi4py, a package that allows Python programs to utilize multiple processors, scikit-learn, an efficient tool with machine learning algorithms, etc. Course content:
1. Introduction to Python (Installation, setup, convenient work in editors)
2. Basic Python Syntax and Data Types
- Variables and data types (e.g., numbers, strings, lists, tuples, dictionaries)
- Operators and expressions
- Control flow statements (e.g., if-else, loops) 3. Functions and Modules
- Defining and using functions
- Built-in functions and modules
- Creating and using custom modules 4. Data Structures (Arrays and Lists)
5. Object-Oriented Programming (OOP) in Python
- Introduction to OOP concepts (e.g., classes, objects, inheritance, polymorphism)
- Creating and using classes in Python 6. Error Handling and Exceptions
- Understanding and handling errors and exceptions in Python
- Exception handling using try-except blocks 7. Working with Scientific Packages: e.g., NumPy, Matplotlib, SciPy, Pandas 8. Advanced Topics: MediaPipe, Mpi4py, scikit-learn, etc. 9. Working examples.
Students are recommended to have their own laptop with Python environments installed, preferably version 3 or higher, and a Python editor such as Jupyter Notebook, Visual Studio Code, or a similar tool. It is also desirable to have Git-related software installed.
Empfohlene Literatur
  • to be announced

Übung zur Veranstaltung

Nummer der Übung
Dozentinnen und Dozenten
  • n. V.

« (zurück) zum Vorlesungsverzeichnis