> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-2.md).

# Week 2 - Data Import and Visualization

### This week we will...

cover the following topics:

* VSCode and GitHub Code Spaces
* AI-assisted programming
* Representation of different data structures
* Reading data from external sources
* Chart and scale types

### Learning Resources

{% file src="/files/q6K3hwYVfwwVvr4TvcNZ" %}

* [Get Started with GitHub Copilot in VS Code](https://www.youtube.com/watch?v=vdBxfFVXnc0)
* [Overview on GitHub Copilot in VS Code](https://code.visualstudio.com/docs/copilot/overview)
* Optional [local installation of Python and VS Code](https://www.datacamp.com/tutorial/setting-up-vscode-python)
* [Examples](https://github.com/opencampus-sh/einfuehrung-in-data-science-und-ml/blob/main/02_Grafische%20Darstellungen/example_plots.ipynb) for the graphical representation of data

### Until next week you should...

* [x] work through [this](https://campus.datacamp.com/courses/data-manipulation-with-pandas/transforming-dataframes) introduction to working with Pandas (only Lesson 1).
* [x] watch [this](https://www.youtube.com/watch?v=dUpyC40cF6Q\&list=PLUaB-1hjhk8FE_XZ87vPPSfHqb6OcM0cF\&index=58) video on importing data as a Pandas DataFrame (18 minutes).
* [x] work through [this](https://app.datacamp.com/learn/courses/introduction-to-data-visualization-with-matplotlib) introduction to creating visualizations with Matplotlib (only Lesson 1).
* [x] watch [this](https://www.youtube.com/watch?v=tFWsuO9f74o) video (4 minutes) to understand the relevance of confidence intervals.<br>
* [x] create a GitHub Codespace and save the files 'kiwo.csv', 'umsatzdaten\_gekuerzt.csv', and 'wetter.csv' from this GitHub repository:\
  <https://github.com/opencampus-sh/einfuehrung-in-data-science-und-ml>
* [x] create a Jupyter notebook that
  * reads the dataset 'umsatzdaten\_gekuerzt.csv' and
  * uses a bar chart to show the relationship of average sales per weekday.
* [x] in a second step, add confidence intervals for the sales per weekday.
* [x] in a further step, sort the weekdays from Monday to Sunday.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-2.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.