Syllabus
Basics of Python, Matlab and R
Loading, storing and displaying data
Simple processing (artefact removal, averaging, interpolation, smoothing)
Fourier transformation (filtering, interpolation)
Fitting (selection of suitable method, determination of parameter uncertainties and correlations, model testing, implementation)
Determination of peak position, width and shape (laser pulse duration measured using two pulse cross-correlation, energy levels in absorption spectra, factorization of Raman spectra)
Single-curve and global analysis (determination of lifetimes from time evolution of triple-minus-singlet spectra, analysis of reaction kinetics of allosteric enzyme)
Selected complex problems (e.g. determination of thermodynamic parameters or equilibrium constants from series of Raman spectra: background removal, SVD, model fitting, model testing)
Annotation
Lecture gives introduction to basic data processing methods using real world datasets typical for chemical physics and biophysics as an example. Some common mistakes and problems of data processing are pointed out.