Students are able to explain and name the advantages and drawbacks of processes for storing and processing extremely large and unstructured quantities of data. They are familiar with modern database technology and can describe the differences to conventional rational databases. Students can define terms and processes such as ETL, data warehouse, data mart, OLAP and Hadoop as they relate to data manage-ment in distributed databases, in streams, in collections for complex structures or for spatially and temporally mobile objects.
Having explored various data storage options, students learn innovative techniques for data analysis, focusing especially on unstructured data including text and web mining, image mining and social network analysis.
Students are able to apply acquired know-how in practical laboratory exercises based on examples from business or academia using software tools.
Having completed the module, student have mastered the principles of conceptual-ising, describing and utilising data management systems for complex analysis pro-jects. They are familiar with complex analysis processes for unstructured data and can provide well grounded justification for the selection of processes and tools re-quired to organise and implement data mining.
Students have furthermore developed a critical appreciation for the results of the procedures' application.

• MPMD 2.2 Advanced Computational Data Analytics

• Evaluated exercise 80 %


• Presentation 20 %