Medicine 1 provides the fundamental medical knowledge necessary for the professional interpretation and use of medical data. The course covers cytology and genetics, as well as the anatomy, physiology, diagnosis, prevention, and treatment of the cardiovascular system, the respiratory system, and the musculoskeletal system. In addition, the course covers the fundamentals of medical decision-making and evidence-based medicine. The goal is to understand medical relationships and to be able to interpret information from clinical contexts in a professional manner.
Programming 1 introduces the fundamental concepts and ways of thinking in software development. Students learn about variables, data types, control structures, arrays, and basic algorithms, and apply them in practical exercises. Another focus is on object-oriented programming, including classes, objects, inheritance, interfaces, and polymorphism. The goal is to design, implement, and test simple programs in a structured manner, as well as to systematically develop algorithmic solutions.
The Introductory Project helps students get started with data science in their medical studies. Working in small teams, students tackle a manageable project with a data science focus and put their initial conceptual ideas into practice. At the same time, the course teaches the fundamentals of independent study and scientific work. Workshops on research, time management, presentation, communication, and exam preparation help students develop effective learning and work strategies.
Medical data must be structured, documented, and searchable. This module therefore covers the fundamentals of medical documentation, terminology, classification, and information retrieval. Students explore semantic indexing and search methods, as well as classification systems such as ICD, OPS, DRG, MeSH, TNM, and MedDRA. The goal is to systematically organize medical information and apply appropriate computational methods for documentation, research, and data processing.
This module teaches fundamental concepts in computer science and lays the foundation for further technical coursework. Topics covered include binary number representations, coding, Boolean algebra, computer architecture, processors, and operating systems. Students learn how information is represented and processed in computer systems and how hardware and software interact. In addition, the fundamentals of scientific work and academic integrity are introduced.
Linear Algebra teaches the mathematical foundations relevant to numerous methods in the field of data science. The course focuses on sets and logic, vectors, matrices, vector spaces, systems of linear equations, linear mappings, as well as eigenvalues and eigenvectors. Students learn to analyze mathematical structures, formally describe applied problems, and apply appropriate solution methods. At the same time, the course fosters the ability to think abstractly and to reason logically.
Medicine 2 expands on the fundamentals of medicine to include key topics in gastroenterology, metabolism, hematology, oncology, and infectious diseases. Students explore anatomy and physiology as well as the diagnosis, prevention, and treatment of relevant diseases. At the same time, the connection to medical data is deepened: medical knowledge should be used, for example, to evaluate medical records in a structured manner and to better understand information technology challenges in hospitals.
Programming 2 builds on the fundamentals covered in Programming 1 and delves deeper into object-oriented software development. Topics include recursion, generic programming, containers, exception handling, graphical user interfaces, threads and synchronization, as well as file and stream processing. Students develop complete programs of small to medium scope and learn to analyze requirements, create designs, use appropriate development tools, and systematically test software.
This module covers the fundamentals of relational databases and their practical application in information systems. Students learn to develop data models, structure databases, and query them using SQL. Topics such as normal forms, transactions, ACID properties, and indexes are covered, as is practical database programming. The goal is to design and implement database systems for complex applications and to use them with appropriate programming interfaces.
This module provides a fundamental understanding of operating systems and computer networks as the technical foundation of modern information systems. Topics covered include processes and threads, user and permissions management, file systems, as well as network architectures and communication protocols. Hands-on exercises with Windows and Linux systems, as well as network components, complement the theoretical material. The goal is to understand the technical fundamentals of complex IT systems and to be able to perform typical configuration and troubleshooting tasks.
Analysis 1 covers the mathematical fundamentals required for many applications in computer science and data science. Topics include functions, limits, continuity, differential and integral calculus, and basic numerical methods. Students learn to analyze mathematical relationships, model simple real-world problems, and solve them systematically. In addition, function graphs and curves are visualized using tools such as MATLAB or Python.
Stochastics covers the fundamentals of probability theory and statistics, which are of central importance for the analysis of medical data. The course covers descriptive statistics, random variables, discrete and continuous distributions, and the basics of statistical testing. In addition, data sets are analyzed using statistical software. Students learn to identify random components, develop appropriate statistical models, and systematically evaluate, interpret, and graphically represent data.
Medicine 3 expands on the fundamentals of medicine to include additional important specialties. Topics covered include the kidneys and urinary tract, the nervous system and sensory organs, endocrinology, and obstetrics and gynecology. The course also covers the fundamentals of pharmacology and clinical trials. Students learn to independently organize medical information and apply it to specific cases. Ethically controversial medical decisions are also examined and critically evaluated from various perspectives.
This module provides a comprehensive overview of information systems in the healthcare sector. Topics covered include hospital information systems, patient and surgical management, medical report documentation, PACS, practice information systems, and electronic patient and health records. Another focus is on interoperability and standards such as HL7/FHIR, DICOM, XML, and EDIFACT. The goal is to understand complex medical information systems, describe requirements, and evaluate possibilities for their integration and communication.
This module provides a basic understanding of the structures, legal framework, and economic processes in the healthcare system. Key topics include hospital organization, financing, funding structures, patient management, and relevant legal principles. In addition, business and legal issues are examined. As a result, students develop an understanding of the organizational context in which medical information systems are used and data is processed.
Technical English prepares students for international communication in the field of data science. Using topics from the fields of medicine and information technology, the course focuses on technical vocabulary, listening and reading comprehension, discussion, presentation, and written communication. Topics include, among others, data management in clinical trials and medical ethics. The goal is to enable students to use English confidently and flexibly in academic and professional contexts at the C1 level.
The Inferential Statistics module teaches the fundamentals of inferential statistics, thereby laying an important foundation for the analysis of medical data. Topics covered include point and interval estimates, confidence intervals, hypothesis testing, and methods for various distributions and sampling scenarios. Sample size estimation and study design are also addressed. Students learn to select appropriate statistical methods, verify their assumptions, and critically evaluate the reliability of statistical conclusions.
Clinical trials are among the key areas of application for data science in medicine. This module covers the fundamentals of planning, conducting, and evaluating clinical trials. Topics include study designs, randomization, sample size calculation, data management, Good Clinical Practice, and the handling of missing values and protocol violations. Special study designs and meta-analyses are also examined. The goal is to plan clinical trials using sound methodology, evaluate them critically, and interpret their results appropriately.
This module combines programming with typical data science tasks. Students are introduced to Python and object-oriented programming and work with Jupyter Notebooks as well as libraries for data analysis and visualization. In addition, the module covers the fundamentals of developing dynamic web applications, REST interfaces, and database access. The goal is to apply data science processes in a practical setting and to independently develop data-driven applications.
This module addresses the specific requirements for data protection and information security as they pertain to medical data and IT systems. In addition to the legal and ethical foundations, the module focuses on the protection of health and social data, as well as technical and organizational measures. Students learn to identify protection needs and risks and to develop solutions that comply with the law. In addition, the module covers the fundamentals of cryptography, encryption, and digital signatures.
This module teaches methods and tools for the professional planning, management, and implementation of projects. Students learn about project phases, roles, and responsibilities, and explore traditional and agile methodologies such as Scrum and Kanban. Planning tools such as Gantt charts and network diagrams are applied in practical exercises. The goal is to organize projects in a goal-oriented manner, coordinate teams, and systematically manage and evaluate project progress.
Data Analytics teaches practical skills in the preparation and descriptive analysis of medical data. Students consolidate data from various sources and prepare it for specific research questions in medical research. Statistical analyses and visualizations are performed using SAS and R. The goal is to systematically analyze medical data, interpret the results from a professional perspective, and present them in a clear and understandable manner.
Epidemiology deals with the distribution of diseases and health-related events in populations, as well as their causes and risk factors. Students learn about epidemiological measures and apply them to real-world study data. This involves data management, descriptive and graphical analyses, and statistical models. The goal is to systematically investigate epidemiological questions, evaluate findings in terms of their validity, and interpret medical study data in an informed manner.
This module covers fundamental machine learning methods and their practical application using Python. Topics include supervised and unsupervised learning methods, model evaluation, data preprocessing, regression, classification, clustering, and ensemble methods. The module also includes an introduction to neural networks and deep learning. Students learn to select appropriate algorithms for specific problems, prepare data, and develop, evaluate, and interpret models in a structured manner.
This seminar teaches key skills for academic work in the fields of medicine and data science. Students learn to conduct targeted research on specialized topics, critically evaluate sources, structure academic texts, and cite sources correctly. They develop their own research questions, write an academic paper, and present their findings in English. In addition, students develop presentation, communication, and time-management skills that are equally relevant for research, academic studies, and professional practice.
In the project work, students work in teams on a more challenging project in the field of data science, applying the technical and methodological skills they have acquired thus far. The projects may address a medical application context and are carried out with a distribution of roles typical of real-world practice. In addition to technical implementation, the focus is on project planning, requirements management, agile work methods, version control, and teamwork. This allows students to gain experience that directly prepares them for future professional activities.
Biostatistics teaches statistical methods for analyzing medical data and builds on the fundamentals of statistics. Students learn to analyze categorical data and event times, as well as to apply methods of linear regression, variance analysis, and covariance analysis. An important component is the practical application of these methods using statistical software and real-world datasets. The goal is to select appropriate biostatistical methods, critically evaluate results, and analyze medical questions in a data-driven manner.
The practical project enables students to apply and deepen the knowledge they have acquired during their studies in a practical semester lasting at least 100 days. They work on real-world tasks in settings such as pharmaceutical companies, hospitals, research institutions, or other data-driven industries. In the process, they gain experience in a professional environment, become familiar with business processes, and can develop their own areas of specialization. The internship semester thus serves as an important bridge between their studies and their future careers.
The practicum semester project documents and reflects on the experiences gained during the practicum semester. Students prepare a written report and present their activities, insights, and findings to their fellow students. In doing so, they learn to systematically analyze medical, scientific, and technical contexts from their day-to-day work and draw conclusions. At the same time, they develop presentation, communication, and reflection skills that are of great importance for their future professional lives.
The bachelor’s thesis marks the completion of the degree program and allows for an in-depth exploration of a specific research question in the field of data science in medicine. Students work independently on a clearly defined task using scientific methods. This includes developing and evaluating potential solutions, carrying out a large-scale project in a structured manner, documenting the results, and finally presenting and discussing them.