Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.
Explores knowledge inference, embedding techniques, and schema matching in data integration.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the overview of information systems, data modeling, managing data, and the distinction between data and information.
Introduces fundamental concepts of data management, including data models, databases, and key tasks.
