Advanced Computational Data Analytics (eMPMD)

Duration1 semester
State of the moduleCompulsory module
Hours of compulsory in credit hours6
Learning outcome/ competencies

In this module, you will learn and deepen your knowledge of current programming languages (such as Python) and use statistical software such as SPSS Statistics and SPSS Modeler to create data mining models. You will get familiar with know the components of the CRISP DM and can explain them.

Successful students will be able to distinguish between structurally validating techniques and structural-recognition techniques. They will also be able to enumerate and distinguish the functionalities of various methods and applications such as factor analysis, cluster analysis and support vector machines. In addition to their systems knowledge and knowledge of applications, students will also be able to precisely and professionally enumerate the pros and cons of various applications.

In the laboratory, you will be able to demonstrate your ability to independently deploy the techniques and algorithms on the computer in practically relevant scenarios. Here, you will be able to clean up and analyze data, critically evaluate the results and, where necessary, select alternative approaches to reach optimal solutions for the problems at hand.

Necessary requirementsNone
Recommended requirementsMPMD 1.2 Foundations of Data Analytics and statistical Programming
Method of examination
(applicable are §§ 9-14 RStPO)   
  • 50% Case study
  • 50% Written assignment