This course is designed to provide students with an opportunity to apply knowledge and skills from the program to a practical problem in an applied setting.
Additionally, students will be educated in the field of data security for heath data.
After the course, students will be able to choose, develop and apply analysis algorithms for various data sources and analysis approaches.
Students understand the specific challenges of medical data analysis.

Project (I) - Prerequisites and Project Domains

The module covers the following topics/contents:
• Data analysis technologies
• Presentation of results
• Evaluation of data quality and reliability

• Lecture script as provided in the course (required)
• M. Nixon and A. Aguado: Feature Extraction and Image Processing for Computer Vision. Academic Press, 2019
• R.C. Gonzalez and R.E. Woods: Digital Image Processing. Pearson, 2017
• C. Solomon: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley, 2019
• C.M. Bishop: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2007
• K. Kiasaleh: Biological Signals Classification and Analysis. Springer, 2015
• E.N. Bruce: Biomedical Signal Processing and Signal Modeling. Wiley. 2001

Integrated course - teaching & discussion, demonstration, practical examples and project work

Written report and in-class presentation of a possible data science project.