Datenanalyse und Versuchsplanung

What it is about

You will learn fundamentals of statistics and probability theory as well as methods of experimental design. Building on this, procedures of data analysis (e.g., cluster methods, principal component analysis, exploratory factor analysis) and model types for structure optimization such as Bayesian networks are covered. In the end you can analyze data from large data sets and create simple model structures for parameter identification in biomechanical/biochemical networks.

What you will be able to do

• Apply statistical and probability-theoretic fundamentals
• Design and evaluate experimental plans
• Use methods of data analysis (clusters, PCA, factor analysis)
• Apply network analysis methods (e.g., network component analysis)
• Construct models for structure optimization (e.g., Bayesian networks)
• Identify parameters in biomechanical/biochemical networks

What the module consists of

• VorlesungConveying mathematical fundamentals, methods of experimental design and data analysis

Teaching method

• TafelanschriebFor presenting mathematical derivations and direct engagement with the fundamentals
• Powerpoint-PräsentationenFor structuring and visualizing lecture content
• Wiederholende Herausarbeitung wesentlicher InhalteFor deepening understanding for application and interpretation of the methods