Techniques and Data Analysis in Biophysics 1
Module version of WS 2020/1
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2021/2||WS 2020/1||WS 2019/20||WS 2018/9||WS 2017/8||SS 2017|
PH2251 is a semester module in German or English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Specific catalogue of special courses for Biophysics
- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
- Complementary catalogue of special courses for condensed matter physics
- Complementary catalogue of special courses for nuclear, particle, and astrophysics
- Complementary catalogue of special courses for Applied and Engineering Physics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||30 h||5 CP|
Responsible coordinator of the module PH2251 in the version of WS 2020/1 was Friedrich Simmel.
Content, Learning Outcome and Preconditions
- The measurement of physical processes
Statistics, measurement uncertainties and error analysis, noise in physical systems, data aquisition (sampling theorem), data filtering
- Atomic force microscopy (AFM)
Basics of AFM-based force spectroscopy and Imaging, Instrumentation and data aquisition, data analysis
- Bayesian statistics
Basics and examples
Upon completion of the module, the students are ablt to:
- utilize basic statistical methods
- critically scrutinize measured data and the apparent uncertainties
- utilize noise in physical systems for experiments
- understand and apply the basics of data sampling and filtering
- describe the basics of atomic force microscopy and how various its applications are
- can understand the basics of Bayesian statistics and are able to apply it to simple problems.
No preconditions in addition to the requirements for the Master’s program in Physics.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Techniques and Data Analysis in Biophysics 1||
Assistants: Pirzer, T.
Fri, 11:00–13:00, ZNN 0.001
|UE||2||Exercise to Techniques and Data Analysis in Biophysics 1||
Responsible/Coordination: Simmel, F.
|dates in groups||
Learning and Teaching Methods
The topics of the lecture are presented by oral, power point and blackboard presentation.
The students should independently work on relevant examples from up-to-date research. These will be discussed scientifically with other examples in the lecture. Here, the students should utilize the learning content of the lecture to analyse and to scientifically evaluate these examples.
In parallel the students should work with text books which can be accompanied by the study of scientific publications.
Lecture notes, additional literature, the lecture notes are provided through Moodle
- P.R. Bevington and D.K. Robinson: Data Reduction and Error Analysis for the Physical Sciences, McGraw-Hill Education Ltd, (2002)
- M.R. Spiegel: Schaum's Outline of Probability and Statistics, McGraw-Hill Education Ltd, (2000)
Description of exams and course work
There will be an oral exam of 25 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using comprehension questions and sample calculations.
For example an assignment in the exam might be:
- What is the difference between Nyquist and 1/f noise?
- What are the imaging regimes in AFM? Explain them!
- What does double emulsion mean?
- Choose a hypothesis test and explain it.
The exam may be repeated at the end of the semester.