Data Science and Artificial Intelligence have become the driving force of many businesses and are an integral part of our daily lives. Small and important decisions or the control of technical processes are based on knowledge gained from collected data. Advice from data driven analytics is integrated into business decisions and is the base of many new business models.

Research in Data Science is progressing rapidly, more and more companies have successfully applied Data Science and Artificial Intelligence in countless scenarios. To account for the close interaction between science and application, the International Data Science Conference brings together researchers, scientists, engineers and entrepreneurs. The 3rd iDSC organized by FH Vorarlberg under the patronage of the Salzburg University of Applied Sciences has given an insight into state-of-the art research topics as well as best practice applications from industries and businesses. New approaches in the fields ofmachine learning, artificial intelligence, data mining and visualization as well as first-hand experience, best practice examples and novel applications from companies have been discussed.

The proceedings include all full papers presented in the scientific track and the short papers from the industry track.

Editors

Peter Haber is a Professor of Information and Communication Technology, in particular for analog and digital signal processing, and responsible coordinator for system theory and electrical engineering at Salzburg University of Applied Sciences. He is a researcher and project manager, leading and coordinating national and international projects in the field of IT and IT management, while also integrating data science solutions at businesses. Since 2009 he has been a member of the international advisory board for the IATED conferences.

Thomas Lampoltshammer is an Assistant Professor for ICT and Deputy Head of the Centrefor E-Governance at the Department of E-Governance and Administration, Danube University Krems, Austria. His current research focus is on the domain of data governance, the effects of ICT application in a connected society, and the effects on a data-driven society. He has a substantial background in the design and implementation of expert and decision-making systems, data analytics, and semantic-based reasoning.

Manfred Mayr is the Academic Program Director for "Business Informatics and Digital Transformation" as well department head for IT-Management at Salzburg University of Applied Sciences. He is a lecturer at international conferences and the author of various publications in the field of business informatics and researches business applications of data science. The digitalisation of ERP applications in the industrial environment is a long-standing and important field of his research. In addition, he has coordinated several national and international research projects.

Kathrin Plankensteiner is the Head of "Data Analytics & Intelligence" at the research center Digital Factory Vorarlberg, FH Vorarlberg University of Applied Science. She studied technical mathematics and data analysis and holds a PhD in Applied Statistics from the University of Klagenfurt (Austria). Her field of research includes reliability testing and analyzing, lifetime modeling, regression analysis, computational statistics, multivariate data analysis, statistical inference, reasoning, & statistical machine learning.

The editors are the conference chairs of the International Data Science Conference.

Organisationen sind bereits von der starren Struktur des klassischen Projektmanagements zu agilen Ans tzen bergegangen. Dies gilt auch f r Softwareentwicklungsprojekte, die flexibel sein m ssen, um schnell auf die W nsche der Kunden reagieren zu k nnen und um nderungen zu ber cksichtigen, die aufgrund von Architekturentscheidungen erforderlich sind. Nachdem sich die Datenwissenschaft als Eckpfeiler in Organisationen und Unternehmen etabliert hat, ist es nun zwingend erforderlich, diesen entscheidenden Schritt auch f r analytische Gesch ftsprozesse durchzuf hren. Die nicht-deterministische Natur der Datenwissenschaft und die ihr innewohnenden analytischen Aufgaben erfordern einen interaktiven Ansatz f r eine evolution re, schrittweise Entwicklung zur Realisierung der wichtigsten Gesch ftsanwendungen und Anwendungsf lle.

Die 3. Internationale Konferenz zur Datenwissenschaft (iDSC 2020) brachte Forscher, Wissenschaftler und Wirtschaftsexperten zusammen, um M glichkeiten zu er rtern, wie neue Wege zur Umsetzung agiler Ans tze in den verschiedenen Bereichen der Datenwissenschaft, wie maschinelles Lernen und KI, Data Mining oder Visualisierung und Kommunikation, sowie Fallstudien und Best Practices von f hrenden Forschungseinrichtungen und Wirtschaftsunternehmen etabliert werden k nnen.

Der Tagungsband umfasst alle im wissenschaftlichen Track vorgestellten Volltexte und die Kurzbeitr ge aus dem studentischen Track.

Zu den Themen, die Sie interessieren, geh ren unter anderem:

• K nstliche Intelligenz und Maschinelles Lernen
• Implementierung von Data-Mining-Prozessen
• Agile Datenwissenschaft und Visualisierung
• Fallstudien und Anwendungen f r Agile Datenwissenschaft

Organizations have moved already from the rigid structure of classical project management towards the adoption of agile approaches. This holds also true for software development projects, which need to be flexible to adopt to rapid requests of clients as well to reflect changes that are required due to architectural design decisions. With data science having established itself as corner stone within organizations and businesses, it is now imperative to perform this crucial step for analytical business processes as well. The non-deterministic nature of data science and its inherent analytical tasks require an interactive approach towards an evolutionary step-by-step development to realize core essential business applications and use-cases.

The 3rd International Data Science Conference (iDSC 2020) brougt together researchers, scientists, and business experts to discuss means of establishing new ways of embracing agile approaches within the various domains of data science, such as machine learning and AI, data mining, or visualization and communication as well as case studies and best-practices from leading research institutions and business companies.

The proceedings include all full papers presented in the scientific track and the short papers from the student track.

Among the topics of interest are:

• Artificial Intelligence and Machine Learning
• Implementation of data mining processes
• Agile Data Science and Visualization
• Case Studies and Applications for Agile Data Science