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Welcome to the Data Science Course! 
This course is designed to provide you with a comprehensive introduction to the field of data science. It is structured into four blocks, each focusing on a different aspect. All concepts and techniques are demonstrated with code examples!
Course overview
- Basics: Introduction to various terms (data science, machine learning, etc.) and data basics such as attribute types.
- Data Preparation & Preprocessing: Data cleaning, integration, and transformation.
- Supervised vs. Unsupervised Learning: Exploration of both terms, and coverage of various algorithms.
- Data Science in Practice: Step-by-step guide to a real-world data science project.
Tools
As always, we use Python and these great packages 
Sneak peek
Here is a sneak peek of selected topics we cover in this course:
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Decision trees
What are decision trees? How do they work?
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Recommender system
With a large Spotify data set, we build a recommender system. At the end you will be able to recommend songs.
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Elbow method
We introduce the elbow method and how it can help us to (for example) refine the recommender system.
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Data Science in Practice
We present a step-by-step guide to a real-world data science project. The project applies concepts, algorithms and techniques introduced up to this point.
... oh, and along the way we cover a different type of transformer.
Expected outcome
By the end of this course, you will have a solid understanding of the data science field. Additionally, you will be capable of preprocessing real-world data, selecting and applying appropriate algorithms to solve practical problems.