The CAS is designed for business professionals, managers, and decision-makers who seek foundational data management skills and insights into AI compliance. Ideal for those in mid to senior roles, including project managers, compliance officers, and data strategy leaders, the program addresses the critical need for data governance and regulatory alignment in the context of the upcoming EU AI Act. Participants should have a basic understanding of business processes and data usage within their organizations but need not possess technical expertise. This course is particularly suited for professionals aiming to integrate AI and data compliance practices into their business strategy. It provides optionally a good basis and preparation for the CAS Data Engineering.

After completing this CAS, you can:

• Understand and apply fundamental concepts of data management based on DAMA-DMBOK principles.
• Build skills in data governance, quality, security, and storage to support business objectives.
• Analyze and implement data modeling, integration, and privacy practices aligned with best practices.
• Gain a thorough understanding of the EU AI Act and its implications for business operations.
• Develop compliance strategies for data usage and AI implementation within organizational frameworks.
• Identify and manage AI risks to ensure ethical and secure AI system usage.
• Collaborate on group projects using a systems-theoretical approach to tackle data governance and compliance challenges.
• Enhance cross-functional alignment within organizations to meet regulatory standards effectively.
• Present project findings through structured colloquia, gaining feedback for continuous improvement.
• Equip participants with actionable strategies for leading AI-compliant data initiatives in their organizations.

This module provides participants with essential data management skills, focusing on principles outlined in the DAMA-DMBOK framework. Participants learn the fundamentals of data governance, quality, modeling, and storage, while exploring how effective data practices support organizational goals. Practical exercises and discussions allow participants to apply these concepts to real-world challenges, developing a strong foundation for compliant and efficient data management. As part of an ongoing group project, they will use a system-theoretical approach to address data challenges within their organizations, culminating in a colloquium where they present their findings and receive feedback for further refinement in Module 2.

Building on data management fundamentals, this module prepares participants for the EU AI Act's compliance requirements and the broader implications for business operations. Topics include AI risk management, compliance frameworks, and cross-functional strategies to align departments with regulatory demands. Group projects continue, with a focus on adjusting strategies for AI regulation, integrating privacy, security, and ethics considerations. Participants apply system-theoretical analysis to identify systemic compliance challenges, which they will address in their projects. The module culminates in a second colloquium, where final presentations showcase their developed solutions, preparing them to lead AI-compliant initiatives in their organizations.

The CAS is characterized by methodological diversity. In addition to lectures, presentations, (group) exercises, case studies or work on practical case studies, great importance is attached to the exchange of experience between participants.

The certificate of achievement consists of a project that accompanies the entire CAS and is worked on in groups. The project work is presented by the groups in a colloquium for each module and discussed in plenary.

The lectures take place on Friday and Saturday. Changes are possible.
 

• Prof. Dr. Pasquale Cirillo: Machine learning
• Dr. Mario Gellrich: Data management and analysis, machine learning
• Dr. Christian Hitz: Data strategy and management, systems theory, system theory applications
• Václav Pechtor: Data management and analysis, machine learning
• Maria Pelli: Systems theory, data management and analysis, machine learning
• Dr. Alexander Walter: Data analysis, machine learning
• Dr. Michael Widmer: Data ethics and security

The certificate course is aimed at graduates of universities (FH/university) with at least three years of professional experience, as well as professionals without a university degree who have at least five years of professional experience and relevant further education qualifications (higher technical college or higher technical examination with a federal certificate/diploma).

Knowledge of English is required.
The course director decides on final admission.

Registrations will be considered in the order in which they are received.