Advanced Computational Data Analytics (eMPMD)
|State of the module||Compulsory module|
|Hours of compulsory in credit hours||6|
|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.
|Recommended requirements||MPMD 1.2 Foundations of Data Analytics and statistical Programming|
|Method of examination|
(applicable are §§ 9-14 RStPO)