intro-to-data-science/README.md

# 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](00_python_in_a_nutshell.ipynb)
- *Chapter 1*: [Python's Scientific Stack](01_scientific_stack.ipynb)
- *Chapter 2*: [A first Example: Classifying Flowers](02_a_first_example.ipynb)
- *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).