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Introduction to Data Analysis Techniques

Module PH2309

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

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
SS 2022WS 2020/1

Basic Information

PH2309 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
  • Complementary catalogue of special courses for condensed matter physics
  • Complementary catalogue of special courses for Biophysics
  • 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 workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Responsible coordinator of the module PH2309 in the version of WS 2020/1 was Allen C. Caldwell.

Content, Learning Outcome and Preconditions


The consists of two parts: 1) Introduction to Statistical Methods Topics: Derivation and application of the most commonly used statistical distributions, Central Limit Theorem, point estimates, confidence intervals, test statistics, p-values and related topics. 2) Introduction to Monte Carlo Methods Topics: Variable transformations, accept-reject methods, sample mean, importance sampling, random walks, Markov Chain Monte Carlos and applications

Learning Outcome

no info


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 Introduction to Machine Learning Heinrich, L.
Assistants: Eller, P.

Learning and Teaching Methods

no info


no info


no info

Module Exam

Description of exams and course work

students turn in solutions to assigned problems for each topic.

Exam Repetition

The exam may be repeated at the end of the semester.

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