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.
- 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
The main goal is to show students how Python can be used to solve typical data science tasks.
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.
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 .
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.
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.