731139 An introduction to scientific programming (in Eng.)
Diese Seite ist erreichbar unter:
- Vorlesung und Übung
- Vortragende/r (Mitwirkende/r)
- Regner, Peter , Schmidt, Johannes
- Angeboten im Semester
- Sommersemester 2022
- Unterrichts-/ Lehrsprachen
This class will introduce the students to Python, one of the most widely used languages in scientific computing today. The class will first introduce students to the basic concepts of programming in Python. Students who are familiar with Python may skip this part. Afterwards, students will learn about the Python scientific ecosystem including numpy, scipy, and pandas. Machine learning techniques from scikit-learn will also be presented, as well as the use of xarray, a library, that allows to access large-scale multi-dimensional data (e.g. climate data).
All programming skills are directly applied to real problems in environmental sciences and environmental economics such as visualizing the growth in CO2-emissions, understanding the impact of COVID19 related lockdowns on electricity demand, and determining which countries tend to heat their homes the most.
- Inhaltliche Voraussetzungen (erwartete Kenntnisse)
- Ability to handle your operating system, including the handling of files and directories
- Basic maths skills
-- Understanding of functions
-- Plotting the graph of a function
-- Statistical basics: calculating means of distributions
- Programming skills are not required, but will be helpful of course. The first three classes are dedicated to basic programming skills and may be skipped by students who have already programmed in Python before.
After taking the class, students
- are able to write code in Python with a particular focus on handling functions, loops, if conditions, and lists
- understand the concept of functions
- know the Python Scientific Ecosystem, including Numpy, Scipy, and Pandas
- are able to download and open data in Python
- are able to plot data and calculate basic statistics in Python
- are to able to use machine-learning algorithms from scikit-learn
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