64 lines
2.9 KiB
Markdown
64 lines
2.9 KiB
Markdown
# An Introduction to Data Science
|
|
|
|
This project is an introductory workshop
|
|
in **[Data Science <img height="12" style="display: inline-block" src="static/link/to_wiki.png">](https://en.wikipedia.org/wiki/Data_science)**
|
|
in the programming language **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
|
|
To learn about Python and programming in detail,
|
|
this [introductory course <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python) is recommended.
|
|
|
|
|
|
### Table of Contents
|
|
|
|
- *Chapter 0*: Python in a Nutshell
|
|
- *Chapter 1*: Python's Scientific Stack
|
|
- *Chapter 2*: A first Example: Classifying Flowers
|
|
- *Chapter 3*: [Case Study: House Prices in Ames, Iowa <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/ames-housing)
|
|
|
|
|
|
### Objective
|
|
|
|
The **main goal** is to **show** students
|
|
how **Python** can be used to solve typical **data science** tasks.
|
|
|
|
|
|
### Prerequisites
|
|
|
|
To be suitable for *beginners*, there are *no* formal prerequisites.
|
|
It is only expected that the student has:
|
|
- a *solid* understanding of the **English** language and
|
|
- knowledge of **basic mathematics** from high school.
|
|
|
|
|
|
### Getting started & Installation
|
|
|
|
To follow this workshop, an installation of **Python 3.8** or higher is expected.
|
|
|
|
A popular and beginner friendly way is
|
|
to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
|
|
that not only ships Python itself
|
|
but also comes pre-packaged with a lot of third-party libraries
|
|
including [Python's scientific stack](https://scipy.org/about.html).
|
|
|
|
Detailed instructions can be found [here <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python#installation).
|
|
|
|
|
|
## Contributing
|
|
|
|
Feedback **is highly encouraged** and will be incorporated.
|
|
Open an issue in the [issues tracker <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-data-science/issues)
|
|
or initiate a [pull request <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://help.github.com/en/articles/about-pull-requests)
|
|
if you are familiar with the concept.
|
|
Simple issues that *anyone* can **help fix** are, for example,
|
|
**spelling mistakes** or **broken links**.
|
|
If you feel that some topic is missing entirely, you may also mention that.
|
|
The materials here are considered a **permanent work-in-progress**.
|
|
|
|
|
|
## About the Author
|
|
|
|
Alexander Hess is a PhD student
|
|
at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
|
|
where he conducts research on urban delivery platforms
|
|
and teaches coding courses based on Python in the BSc and MBA programs.
|
|
|
|
Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).
|