> 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-1.md).

# Week 1 - Introduction to Data Science

### This week you will...

get an introduction to the following topics:

* What is data science?
* R vs. Python vs. SPSS vs. ...
* Jupyter Notebooks

### Learning Resources

{% file src="/files/XVPYuvR2tX58Za4PILM7" %}

* [Markdown Guide](https://colab.research.google.com/notebooks/markdown_guide.ipynb)

### Until next week you should...

* [x] Register for GitHub Copilot as described [here](https://docs.github.com/en/copilot/quickstart) or download Cursor (includes a 1 week Pro trial) as described [here](https://cursor.com/download) to use it instead of VS Code (Cursor and VS Code are mainly identical).\
  As a student or teacher, you get free access to GitHub Copilot Pro following the instructions given [here](https://docs.github.com/en/copilot/managing-copilot/managing-copilot-as-an-individual-subscriber/getting-started-with-copilot-on-your-personal-account/getting-free-access-to-copilot-pro-as-a-student-teacher-or-maintainer).<br>
* [x] watch [this video](https://www.youtube.com/watch?v=Ctqi5Y4X-jA\&list=PLTjRvDozrdlxj5wgH4qkvwSOdHLOCx10f\&index=11) on working with strings (16 minutes)<br>
* [x] watch [this snippet](https://www.youtube.com/watch?v=yE9v9rt6ziw\&list=PLTjRvDozrdlxj5wgH4qkvwSOdHLOCx10f\&index=3\&t=3073s) on working with numbers (“Numbers” and “Working With Numbers”; 5 minutes)<br>
* [x] watch the first four chapters of [this video](https://www.youtube.com/watch?v=u-OmVr_fT4s\&list=PLTjRvDozrdlxj5wgH4qkvwSOdHLOCx10f\&index=8) on functions\
  (12 minutes)<br>
* [x] watch [this video](https://www.youtube.com/watch?v=9OeznAkyQz4\&list=PLTjRvDozrdlxj5wgH4qkvwSOdHLOCx10f\&index=11) on working with lists in Python\
  (18 minutes)<br>
* [x] watch [this video](https://www.youtube.com/watch?v=mpk4Q5feWaw) on setting up VS Code, specifically for Python and Data Science<br>
* [x] do the followings tasks:

  * Import the dataset "wetter.csv", which you can download via the following link:\
    <https://raw.githubusercontent.com/opencampus-sh/intro-to-data-science-and-ml/main/data/bakery/wetter.csv>
  * Calculate the overall average temperature.
  * Calculate the average temperature for the month of July.
  * Compare whether the months of July and May differ significantly in their average temperature.<br>

  *Remember that you can use Claude or ChatGPT for support at any time. Especially with relatively simple tasks, you can learn well how to solve assignments that you have never done before.*


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-1.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.