The central repository for our ser. Here, you will find all teaching material colsolidated.
- Nothing so far...
While this course does not come with a list of mandatory readings, we will often refer to some central resources in R and python, which for the most part can always be accessed in a free and updated online version. We generally recommed you to use these amazing ressources for problem-solfing and further self-study on the topic.
- Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc. Online available here
- Baumer, B., Kaplan, D. & Horton, N. (2020) Modern Data Science with R (2nd Ed.). CRC Press Online available here
- Kuhn, M., Silge, J. (2020) Tidy Modeling with R Online available here
- VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc. Online available here
The course is structured in 3 modules, where their content can be found in the corresponding subfolders
This module will prove a condensed introduction to the “Data Science Pipeline”, introducing students to methods, techniques, and workflows in applied data analytics and machine learning, including data acquisition, preparation, analysis, visualization, and communication.
Focuses on analyzing a variety of unstructured data sources. Particularly, students will learn how to explore, analyze, and visualize natural language (text) as well as relational (network) data.
Introduces to the most recent developments in machine learning, which are deep learning and artificial intelligence applications. The module will provide a solid foundation for this exciting and rapidly developing field. Students will learn whether and how to apply deep learning techniques for business analytics, and acquire proficiency in new methods autonomously.