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Welcome to the Python Extensive Course! 👋 This course will teach you the Python programming language.

lattenspitze code
lattenspitze plotly
Creation of a 3D surface plot of the Lattenspitze. 🏔️
That's the power of Python - ease of use paired with a wide range of functionalities stemming from a large developer community! 🦾

Why Python?

  • Ease of use

    Python with its syntax is easy to learn and yet very powerful.

  • Flexible

    Python is a versatile language that can be used for data analysis, automation, artificial intelligence, and many more applications.

The below section should give you an impression of what you can do with Python. This is not an extensive list by all means. It might sound trashy but if you can imagine something you probably can build it in Python.

Info

Don't worry about the code snippets too much, after finishing the course you'll have a better understanding and will be able to run and modify code yourself. For now, the following snippets illustrate the capabilities of the language and what complex things you can achieve with little code. There is no need to execute it - Just take a look!

Examples

Just the beginning...

All of the following examples are from one of the courses featured on our website. If you stick around and explore subsequent Python courses you will be able to easily implement all examples yourself! 🚀

Machine Learning

With Python you can easily train your own machine learning models. In this example, with just a few lines of code, one such model (a decision tree) is fit and visualized1.

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import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.tree import DecisionTreeRegressor, plot_tree

# load data
X, y = fetch_california_housing(return_X_y=True, as_frame=True)

# fit a decision tree
tree = DecisionTreeRegressor(
    random_state=784, max_depth=2, min_samples_leaf=15
)
tree.fit(X, y)

# visualize the tree
plot_tree(tree, filled=True, feature_names=X.columns, proportion=True)
plt.show()
Decision Tree
A decision tree visualized.

Computer Vision/AI

Or how about state-of-the-art computer vision with YOLO2?

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from ultralytics import YOLO

# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")

# Run inference on the source
results = model("https://ultralytics.com/images/bus.jpg")
results[0].show()
Computer Vision
Object detection with YOLO.

Automation

But it's not just machine learning and AI, you can also automate mundane tasks. This code snippet fetches some data (from an online service) and writes an Excel file3.

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import pandas as pd
import requests

url = "https://api.coincap.io/v2/assets/pepe-cash/history?interval=d1"
response = requests.get(url)

data = pd.DataFrame(response.json()["data"])
data.to_excel("price-history.xlsx", index=False)

Visualizations

You can create stunning and interactive visualizations4. Let's visualize the above written Excel file.

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import pandas as pd
import plotly.express as px

data = pd.read_excel("price-history.xlsx")
fig = px.area(
    data_frame=data,
    x="date",
    y="priceUsd",
    title="Price History in USD",
    color_discrete_sequence=["#009485"],
)
fig.show()

Web Development

You can create websites, just like this one. In fact, all the heavy lifting of this site is done by Python and tools developed by its community.

The most important package used to build this site was Material for MkDocs, a widely used and customizable static site generator. ❤

Getting Started...

In the next sections, we will install Python including the code editor Visual Studio Code.

Tip

Both Python and Visual Studio Code are already pre-installed on PCs in the MCI computer rooms. If you are working with your own computer, please proceed to the next page.

  1. Scikit-learn is a Python package for machine learning. ↩

  2. YOLO is an advanced real-time object detection model known for its speed and accuracy, enabling efficient identification and localization of objects within images and videos. ↩

  3. requests is a Python package to interact with APIs. ↩

  4. Plotly is a Python graphing package that lets you create interactive, publication-quality graphs. ↩