We are proud to have presented an impressive range of talks! The keynotes and technical talks were given by experienced data scientists from both academia and industry. The program was completed by a poster session featuring promising students who want to share their latest data science projects with the audience. Kubernetes has become the default container orchestrator framework, setting the standards for application deployment in a distributed environment.
In the past years, numerous tools have been developed to extend Kubernetes capabilities and enhance its features. Simultaneously, the expansion of the technology landscape prompted the growth of the adopter base and the number of scenarios where cloud native can be applied. The organic adoption and development of new tools, created the ecosystem and community as we know it today. It will present the interoperability of tools, inclusivity at the community and adopters level, and a culture of change and education that drives the ubiquity of the cloud native.
In this presentation, we will understand what is ethics in the context of data, why it matters, and especially, why you as a data scientist should care. Where and why is explainability needed? And what kinds of methods are out there to achieve explainability? Nina Meinel Springer Nature. Attribution Modeling is the holy grail in Marketing and simply, it is the analytical science of determining which marketing tactics are contributing to sales or conversions and understand the user journey.
A very common approach is last click, which is easy and understandable. Given the data there is much more a data scientist can do using Machine Learning, Simulation approaches, adding time components and so on. Still the most important is being able to easily interpret the impact of different touchpoints within the journey to truly enable marketers.
In the talk we will show how this can be done using a real example. Jae Sook Cheong University of Bayreuth. Especially in the times when artificial intelligence reshapes human activities and lives, we cannot overstress the importance of learning for humans. We believe that knowledge structure graphs provide a great framework for effective learning, especially abstract and complicated knowledge, and for discovering new insights. Even though these experiments are designed to detect gamma-rays, their measurements are dominated by an enormous number of cosmic-ray background events.
