Data Driven Studies - quantitative methods of future studies
Quantitative and data-driven methods, which make use of large data sets for futures studies, have become increasingly important in recent years and are being applied, discussed and further developed more and more in the relevant community.
This course will introduce the current research topic of Data Driven Foresight and its state of research. In addition to the opportunities presented by the use of data in this context, the challenges and limitations of this approach will be addressed.
After a short connection and classification of data-driven foresight in the overarching topic of futures studies, the various methods in this area are discussed. These include informetric approaches stemming from the field of bibliometrics or patentometrics, which utilize the statistical analysis of publication and patent data for foresight (especially technology foresight). In addition, it discusses how these approaches can be used for scientific literature research.
Based on this, methods from the field of data mining, network analyses and machine learning are discussed. Subsequently, further methodological approaches from the field of text mining and computational linguistics are explained. In addition, it will be explained and discussed which approaches exist and can be used to visualise the data and analysis results. The methods are presented and discussed with regard to both the theoretical foundations and the concrete application in projects, as well as the interpretation of the results.
|15.11.2021||9:00 - 16:00||Dr. Marcus John||moodle|
|29.11.2021||9:00 - 16:00||Dr. Marcus John||moodle|
|6.12.2021||9:00 - 16:00||Dr. Marcus John||moodle|