Alexander Hess
46e8b9e174
- expand section on indexing and slicing in Chapter 1 on arrays - add section on dimensionality and shapes of arrays - streamline text in Chapter 1 - include thumbnails in links to scientific libraries - add explanation on slicing to core Python introduction and adjust the list example - streamline some text and titles in Chapter 0 |
||
---|---|---|
00_python_in_a_nutshell | ||
01_scientific_stack | ||
03_classification | ||
static/link | ||
.gitignore | ||
LICENSE.txt | ||
poetry.lock | ||
pyproject.toml | ||
README.md |
An Introduction to Data Science
This project is an introductory workshop in Data Science in the programming language Python . To learn about Python and programming in detail, this introductory course is recommended.
Table of Contents
- Chapter 0: Python in a Nutshell
- Content: Basic Arithmetic
- Exercises: Python as a Calculator
- Content: Business Logic
- Exercises: Simple Loops
- Exercises: Fizz Buzz
- Content: Functions
- Exercises: Volume of a Sphere
- Content: Data Types
- Chapter 1: Python's Scientific Stack
- Chapter 2: Time Series Analyis
- Chapter 3: A first Example: Classifying Flowers
- Chapter 4: Case Study: House Prices in Ames, Iowa
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 that not only ships Python itself but also comes pre-packaged with a lot of third-party libraries including Python's scientific stack.
Detailed instructions can be found here .
Contributing
Feedback is highly encouraged and will be incorporated. Open an issue in the issues tracker or initiate a pull request 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 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.