Advanced Computational Data Analytics

Advanced Computational Data Analytics

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

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.

You will gain proficiency in the use of computer packages such as SPSS Statistics and/or SPSS Modeler or SAS Enterprise Miner.

In the laboratory, you will be able to demonstrate your ability to independently deploy the techniques and algorithms on the computer. 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.

Students will acquire the capacity to enrich data in a well-founded manner through the computation of new parameters. Students will also be able to demonstrate proficiency in the use of vector support machines via the example of medical data. Students will employ this particular method to generate practical, medically viable results such as activity phases on the basis of EEG data.

Further, students will acquire the capacity to successfully generate questionnaires and surveys, with due regard paid to scholarly and scientific insights. Students will have the opportunity to collect and evaluate data using complex statistical techniques such as factor analysis and structural equation modeling, within the framework of a case study. Ultimately, students will be called upon to propose well thought-out and practical business actions from the statistical results. 

Necessary requirementsNone
Recommended requirementsMPMD 1.2 Foundations of Data Analytics and statistical Programming
Method of examination
(applicable are §§ 9-14 RStPO)   
  • 50% Presentation
  • 50% Project report