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Under the successful completion of the module, students understand the basics of data management, which are used in the area of data analysis. They are able to deal systematically with data and metadata and have the ability to organize and manipulate data. In addition, they learned selected aspects of conversion, quality assurance, reuse and retention of data.

The course offers an introduction to data management for beginners. As the course is dedicated to an interdisciplinary group of students, it gives an overview of methods to cover aspects related to and applicable within different scientific fields. It focuses on practical aspects and provides theory to the extent required to understand data management and apply them in an effective way. 

In the course, different levels of data structuration are explained (structured, semi-structured, unstructured). For each level example methods to collect, organize, store and manipulate data are presented. Moreover, guidelines to select appropriate storage models for data and conversion between different data structures are introduced. For a deeper understanding of data, different types of metadata are introduced together with methods to deal with them. 

Additionally, selected topics from data management, information lifecycle management, as well as topics related to data quality and data security are presented and practised on example scenarios. 

The course consists of the theoretical part (lecture) enriched with demonstrations of selected tools and practical exercises on example datasets. 

Course assessment is based on regular written and/or oral contribution by participants. Details will be provided in the first slot.

No prior experience in data management is required. However, programming experience, either in Python or R, is necessary for the successful completion of the practical part of this course. Thus, it is recommended to take this course after the Introduction to Programming (module 1 of the Minor Digital Science). If you completed other programming course in Python or R please put the course number and semester as a remark in your registration.

You may work on the university computers (12) or your laptops with an IDE for Python or R installed. 

Remarks on the schedule: there is one slot on Tuesday (see Dates). Presence is mandatory; thus, you may skip only a few slots. Ensure you have no scheduling conflicts for the mid-term exam (see Dates) and the final presentation (the last session).

This course is for all bachelor students and master students excluding Computer Science students and master students in Information Systems. 

The acceptance procedure is based on prioritised randomisation. Students advanced in completion of the Digital Science minor get precedence, especially these who passed module 1.