Data Analysis
Module PH2221
Module version of SS 2015
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 2019/20 | WS 2017/8 | SS 2015 |
Basic Information
PH2221 is a semester module in 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 nuclear, particle, and astrophysics
- Specific catalogue of special courses for Applied and Engineering Physics
- Complementary catalogue of special courses for condensed matter physics
- Complementary catalogue of special courses for Biophysics
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 | 75 h | 5 CP |
Responsible coordinator of the module PH2221 in the version of SS 2015 was Allen C. Caldwell.
Content, Learning Outcome and Preconditions
Content
In this series of lectures, we will introduce the concept of probability, develop the basic statistical distributions and provide a detailed discussion of data analysis. Example analysis tasks from current physics topics will be carried out in detail. Both frequentist and Bayesian methods will be taught. The lectures will be held in English.
Learning Outcome
You will learn how to formulate your statistical data analysis, from identifying the correct underlying statistical model, to defining your data probability functions (likelihoods) and prior probabiltiies. You will learn when Gaussian approximations are valid, the derivation and use of the Central Limit theorem, how to define and use test statistics, goodness-of-fit tests, model selection, etc.
Preconditions
You will be expected to have a solid background in undergraduate mathematics (Fourier transforms, differential equations, stadard integrals, etc.). You are also expected to be able to program a computer, and have access to a computer, since many of the assignments will require numerical work.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Data Analysis | Caldwell, A. |
Mon, 16:00–18:00, PH II 127 |
eLearning |
| UE | 2 | Exercise to Data Analysis |
Krätzschmar, T.
Responsible/Coordination: Caldwell, A. |
dates in groups |
eLearning |
Learning and Teaching Methods
The material will be presented in lectures, and exercises will be given for you to work on. The exercises will be a mix of analytical calculations and numerical calculations for which you will need to do some computer work. There will be a weekly recitation where the exercises and further material will be discussed.
Media
Lecture notes will be provided. You will additionally need a computer.
Literature
A script for the course will be provided.
Module Exam
Description of exams and course work
In a written exam the learning outcome is tested using comprehension questions and sample problems.
In accordance with §12 (8) APSO the exam can be done as an oral exam. In this case the time duration is 25 minutes.
Exam Repetition
The exam may be repeated at the end of the semester.