Recent technological advancements in the domains of Artificial Intelligence, Internet of Things and Production 4.0 have generated a fertile ground for plentiful data science investigations. In parallel data science developments have also gained momentum at several industrial settings to support and improve production outputs as well as market performances.
The yearly International Data Science Conference (iDSC) 2021, in its fourth edition, virtually presented by AIT Austrian Institute of Technology on 20th an 21st October, therefore followed once more its founding mission, to team scientists and researchers on one hand and industrial practitioners on the other to common discuss the latest data science innovations from a diverse range of research backgrounds and to showcase new implementations from factory shop floors or from sensor driven applications in the field.
The six thematic sessions of the conference have been mirroring allthe top issues of the data science discipline ranging from challenges in the industrial setting, via Deep and Machine learning methodologies and NLP approaches up to future innovation strategies. While the Research Track had a strong emphasis on Safety and Security matters like Anomaly Detection, Integrity Awareness or Ethical fairness of AI e.g., the Industry Track more made deep reflections on the Scaling of Business models, Infrastructure and Software, the Use of Data Science in SMEs, or the development of Data Ecosystems as an Incubator for Data Innovation.
The conference proceedings contain the peer-reviewed full papers of the research track and the corresponding German abstracts as well as short papers from the industry track.

The Editors
Peter Haber is Assistant Professor of Information and Communication Technology, in particular for analog and digital signal processing, and responsible coordinator for system theory andelectrical 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 Centre for 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. Helmut Leopold is the Head of Center for Digital Safety&Security at the AIT Austrian Instituteof Technology and is responsible for research areas such as artificial intelligence and cyber security. Prior to AIT, Mr. Leopold was at Alcatel and at Telekom Austria where he played a major role in the digitalization transformation of the organisation. He graduated in computer science from the TU Vienna and holds a PhD from the Lancaster University in England.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 business applications. 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.

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 4th International Data Science Conference (iDSC) 2021 brought 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 corresponding German abstracts as well as 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

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Organisationen sind bereits von der starren Struktur des klassischen Projektmanagements zu agilen Ansätzen übergegangen. Dies gilt auch für Softwareentwicklungsprojekte, die flexibel sein müssen, um schnell auf die Wünsche der Kunden reagieren zu können und um Änderungen zu berücksichtigen, 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 für analytische Geschäftsprozesse durchzuführen. Die nicht-deterministische Natur der Datenwissenschaft und die ihr innewohnenden analytischen Aufgaben erfordern einen interaktiven Ansatz für eine evolutionäre, schrittweise Entwicklung zur Realisierung der wichtigsten Geschäftsanwendungen und Anwendungsfälle.

Die 4. Internationale Konferenz zur Datenwissenschaft (iDSC 2021) brachte Forscher, Wissenschaftler und Wirtschaftsexperten zusammen, um Möglichkeiten zu erörtern, wie neue Wege zur Umsetzung agiler Ansätze in den verschiedenen Bereichen der Datenwissenschaft, wie maschinelles Lernen und KI, Data Mining oder Visualisierung und Kommunikation, sowie Fallstudien und Best Practices von führenden Forschungseinrichtungen und Wirtschaftsunternehmen etabliert werdenkönnen.

Der Tagungsband umfasst alle im wissenschaftlichen Track vorgestellten Volltexte und die Kurzbeiträge aus dem studentischen Track auf Englisch und die dazugehörigen Abstracts auf Deutsch.

Zu den Themen, die sie interessieren, gehören unter anderem:

• Künstliche Intelligenz und Maschinelles Lernen
• Implementierung von Data-Mining-Prozessen
• Agile Datenwissenschaft und Visualisierung
• Fallstudien und Anwendungen für Agile Datenwissenschaft