After successful completion of the course, students are able to understand the major principles, methods, concepts, and techniques for data generation and analysis in medicine and life sciences and will have acquired a critical understanding of their theories and principles.

By the end of this course, students should be able to:

On the "Data Generation" page:
- Describe the major anatomical aspects of medicine, the natural life processes and normal biochemical functioning of the organism (physiology), and abnormal and pathological conditions and their causes (pathology).
- Describe the chemistry of major biomolecules such as nucleic acids, proteins, carbohydrates, lipids, and other metabolites, and explain biological structures and describe structure-function interactions.
- Discuss the data generated or acquired in informatics relevant to medicine and the life sciences. This includes biomedical data, biosignals, medical images, epidemiological and public healthcare data.

On the Analytics page:
- Describe the key methods required to process and analyze data from informatics for medicine and life sciences. This includes basic methods from statistics, computer vision, image processing, data visualization, and machine learning.
- Which methods are suitable for processing and analyzing specific data from different aspects.
- Understand the needs of stakeholders (e.g., patients, clinicians, bioinformaticians, public health/biology/biochemistry researchers) and apply the above methods to support workflows.

• Frontal Lectures 
• Invited Lectures
• Excursion
• Practical Assignments (x5)

ECTS-Breakdown: 6 ECTS = 150 hours

55 hours –  Lectures (incl. Invited lectures and excursion)

45 hours – Practical assignments (in groups)

15 hours  – Preparation for the oral discussion of the practical assignments

35 hours –  Exam preparation 

• Written Exam (60%)
• Practical Assignments (5 x 6% = 30%)
• Oral Discussion of the Practical Assignments (10%)