Introduction to Data Analysis

Signal processing basics I (Introduction)
Signal processing basics II (analogous and digital data storage; sampling rates; resolution)
Statistical parameters (Signal-noise-ratio; Pearson’s R-value; Crystallographic R-values)
Statistical parameters (distribution functions; weighting factors)
Data manipulation (back-ground subtraction, data smoothing)
Fourier transforms (Resolution, sampling rates, window functions) I
Fourier transforms (Resolution, sampling rates, window functions) II
Signals and their description (Peak functions)
Discrimination of signals from measurement background
Numerical evaluation of the similarity between different data I
Numerical evaluation of the similarity between different data II
Autocorrelation I
Autocorrelation II
“Big data”, neural networks, machine learning and artificial intelligence

Understanding of statistical and data evaluation techniques.

Written exam

Klausur

Will be anounced in the lecture.

1st SWS: Signal processing basics I (Introduction)
2nd SWS: Signal processing basics II (analogous and digital data storage; sampling rates; resolution)
3rd SWS: Statistical parameters (Signal-noise-ratio; Pearson’s R-value; Crystallographic R-values)
4th SWS: Statistical parameters (distribution functions; weighting factors)
5th SWS: Data manipulation (back-ground subtraction, data smoothing)
6th SWS: Fourier transforms (Resolution, sampling rates, window functions) I
7th SWS: Fourier transforms (Resolution, sampling rates, window functions) II
8th SWS: Signals and their description (Peak functions)
9th SWS: Discrimination of signals from measurement background
10th SWS: Numerical evaluation of the similarity between different data I
11th SWS: Numerical evaluation of the similarity between different data II
12th SWS: Autocorrelation I
13th SWS: Autocorrelation II
14th SWS: “Big data”, neural networks, machine learning and artificial intelligence