Modern Statistical data analysis for practitioners

Presentation of the course open to all interested students

20 October 2017 10:00 c.t.
Seminarraum __.401
Fachbereich 13 Physik,
Institut für Biophysik,
Max-von-Laue-Str. 1, Goethe-Uni Frankfurt
Dozenten: Dr. Roberto Covino, Dr. Jürgen Köfinger, Dr. Lukas Stelzl


Content of the course
We introduce the basics of probability theory, classical statistics, and
classical error analysis (p-values, confidence intervals), which serves
as the starting point to explore modern methods of statistics (Maximum
Likelihood, Bayes). We use these methods to extract information from
noisy data through (non-) linear parameter estimation (fitting) and
model comparison. We show how to analyze data containing dynamical
information by time series analysis (correlation functions, error
analysis) and Markov-Chain models and kinetic models described by rate
equations. We introduce and discuss clustering methods to analyze
high-dimensional data.

 1. Crash course in statistics
     1. Elements of probability theory
     2. Central limit theorem and error of the mean
     3. Classical error analysis and error propagation
     4. Confidence intervals and p-values
     5. Statistical tests
     6. Maximum likelihood estimation
     7. Bayesian inference
 2. Model fitting
     1. Linear models
     2. Non-linear models
     3. Model comparison
 3. Time series analysis
     1. Autocorrelations
     2. Block-averaging
     3. Bootstrapping / Jackknifing
 4. Markov-chains and kinetic models
     1. Master equation
     2. Monte Carlo sampling
     3. Uncertainty quantification using Monte Carlo sampling
 5. Clustering

Goal of the course
To overarching goal is to equip the students with the necessary
statistical tools to extract information from noisy data reliably and
with quantified uncertainties. The students should be able to identify
the common pitfalls of statistical data analysis in their own work and
be able to critically assess the quality of published data and
statistical analysis. These goals will be practised in the practical
course on real world examples.

Please check also the following... Link


Max Planck Institute of Biophysics

Statistical data analysis for practitioners
Dr. Roberto Covino
Dr. Jürgen Köfinger
Dr. Lukas Stelzl
Department of Theoretical Biophysics

Phone: +49 (0) 69 6303-2503
E-Mail: roberto.covino(at)
E-Mail: juergen.koefinger(at)
E-Mail: lukas.stelzl(at)

Course documentation

  • Course calendar (pdf)
Course announcement