78 lines
3.9 KiB
Markdown
78 lines
3.9 KiB
Markdown
# An Introduction to Data Science
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This project is an introductory workshop
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in **[Data Science <img height="12" style="display: inline-block" src="static/link/to_wiki.png">](https://en.wikipedia.org/wiki/Data_science)**
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in the programming language **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
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To learn about Python and programming in detail,
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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.
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### Table of Contents
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- *Chapter 0*: **Python in a Nutshell**
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- *Content*: [Basic Arithmetic](00_python_in_a_nutshell/00_content_arithmetic.ipynb)
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- *Exercises*: [Python as a Calculator](00_python_in_a_nutshell/01_exercises_calculator.ipynb)
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- *Content*: [Business Logic](00_python_in_a_nutshell/02_content_logic.ipynb)
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- *Exercises*: [Simple Loops](00_python_in_a_nutshell/03_exercises_loops.ipynb)
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- *Exercises*: [Fizz Buzz](00_python_in_a_nutshell/04_exercises_fizz_buzz.ipynb)
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- *Content*: [Functions](00_python_in_a_nutshell/05_content_functions.ipynb)
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- *Exercises*: [Volume of a Sphere](00_python_in_a_nutshell/06_exercises_volume.ipynb)
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- *Content*: [Data Types](00_python_in_a_nutshell/07_content_data_types.ipynb)
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- *Chapter 1*: [Python's Scientific Stack](01_scientific_stack/00_content.ipynb)
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- *Chapter 2*: **Time Series Analyis**
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- *Chapter 3*: [A first Example: Classifying Flowers](02_classification/00_content.ipynb)
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- *Chapter 4*: [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)
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### Objective
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The **main goal** is to **show** students
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how **Python** can be used to solve typical **data science** tasks.
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### Prerequisites
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To be suitable for *beginners*, there are *no* formal prerequisites.
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It is only expected that the student has:
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- a *solid* understanding of the **English** language and
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- knowledge of **basic mathematics** from high school.
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### Getting started & Installation
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To follow this workshop, an installation of **Python 3.9** or higher is expected.
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A popular and beginner friendly way is
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to install the [Anaconda Distribution](https://www.anaconda.com/download)
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that not only ships Python itself
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but also comes pre-packaged with a lot of third-party libraries
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including [Python's scientific stack](https://scipy.org/).
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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).
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If you are *not* using the Anaconda Distribution,
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you must install the third-party libraries via the command
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`pip install -r requirements.txt` (or something equivalent)
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before working with the notebook files.
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## Contributing
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Feedback **is highly encouraged** and will be incorporated.
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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)
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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)
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if you are familiar with the concept.
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Simple issues that *anyone* can **help fix** are, for example,
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**spelling mistakes** or **broken links**.
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If you feel that some topic is missing entirely, you may also mention that.
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The materials here are considered a **permanent work-in-progress**.
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## About the Author
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Alexander Hess is a PhD student
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at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
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where he conducts research on urban delivery platforms
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and teaches coding courses based on Python in the BSc and MBA programs.
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Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).
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