1. Learning the concepts of data science, machine learning, and deep learning
2. Translating business problems into analytical questions and solving them using machine learning
3. Selecting, implementing, and evaluating machine learning algorithms using Python
4. Principles of supervised and unsupervised machine learning
5. Operationalizing machine learning and (generative) AI systems in organizations (MLOps)

This hands-on module reflects an introduction to data science, machine learning, and deep learning.  We will address fundamental concepts for data-driven decision making and data-analytical thinking by following a case-based approach. Using widespread and state-of-the-art tools for big data and artificial intelligence (e.g., Python), we provide an introduction into machine learning and the concepts of supervised and unsupervised learning. Participants will get practical experiences in implementing and evaluating state-of-the-art algorithms for analyzing structured and unstructured data such as text. As opposed to most big data and artificial intelligence course, this module is not taught in a method-oriented fashion, but follows a case study logic – we choose a variety of real-world business problems and solve them the analytics way. Furthermore, we teach you how to operationalize machine learning systems in your organization using MLOps.