The program aims to provide an in-depth understanding of the essential aspects of data-based decision-making and the skills required to apply and implement these powerful methods in a successful and responsible manner. Apart from the necessary programming skills, this comprises:

• methods of data acquisition both from the internet and from sensors;
• methods to efficiently store and access data in large and distributed data bases;
• statistical model building including a wide range of data mining methods, signal processing, and machine learning techniques;
• visualization of relevant information;
• construction and use of confidence intervals, hypothesis testing, and sensitivity analyses; 
• know the legal foundations of data engineering;
• methods to ensure data security and privacy;
• awareness of the societal and ethical implications of digitization;
• scientific qualification;
• competence to take up qualified employment in Data Engineering;
• competence for responsible involvement in society; 
• personal growth.
   

By the end of the online program students will be able to:

1. critically assess and creatively apply technological possibilities and innovations driven by big data;
2. acquire data from sensors and use microcontrollers to process  data and to transmit them to databases on servers or the internet in general;
3. set up and use databases to efficiently and securely manage and access large amounts of data;
4. apply statistical concepts and use statistical models in the context of data analytics; 
5. use, adapt and improve visualization techniques to support data-based decision-making;
6. design, implement and exploit various representations of data for classification and regression including supervised machine learning methods and core ideas of deep learning; 
7. apply and critically assess data acquisition methods and analytical techniques in  practical scenarios  in organizations and companies;
8. independently investigate complex problems and undertake scientific or applied research into a specialist area utilizing appropriate methods, also taking methods and insights of other disciplines into account; 
9. professionally communicate their conclusions and recommendations, the underlying information and their reasons to both specialists and non-specialists, both clearly and unambiguously based on the state of research and application; 
10. assess and communicate social, scientific and ethical insights that also derive from the application of their knowledge and their decisions; 
11. engage ethically with the academic, professional and wider communities and actively contribute to a sustainable future;
12. take responsibility for their own learning, personal development, and role in society, evaluating critical feedback and self-analysis;
13. adhere to and defend ethical, scientific and professional standards.