Students should have a basic understanding of the digitalization process and the role of IS within it. Basic programming skills are desirable for analyzing big datasets.
Upon completion of the course, the students will be able to:
Provide an initial understanding of data science and its fundamental principles by offering a high-level overview of concepts and principles
Foster data-analytic thinking and explain how to extract knowledge from different types of data
Discuss why and how the change in the digital era and data availability can transform business and society
Understand the notions of digital transformation, data science, big data and analytics, their relations and their differences.
Explore data-centered business problems, propose and develop data-driven business models, strategies, and solutions
Evaluate and assess practical applications of (big) data and analytics
Create a common understanding that will lead to more efficient communication between management, technical/development, and data science teams.
The cross-disciplinary nature of Information Systems can be the driving force to give meaning to the massive amounts of data generated every moment and improve the relation among data and business models. The course builds on concepts and techniques from multiple fields including business, management, economics, sociology, computer science, philosophy. The students will be able to have the broadest perspective on real life problems, view a challenge as a whole taking into account different perspectives and see how different pieces fit together leading them to propose, design or develop data-driven solutions. The course introduces the students in data science and the role of IS in digital transformation. The course will develop the conceptual foundations, frameworks and methods for analyzing the relationships between organizations and data. The course gives students a systematic basis for addressing change in the digital business, and bridging digital transformation with digital sustainability for shared value that impacts society as a whole. Focus will be given on discussing big data analytics ecosystems and strategies for digital transformation as paths to change and disruption.
Up to 6 hours of lectures/organized group work per week. Group work including project based mandatory exercises/presentations. Standard work load for 7,5 ECTS courses is 210 hours per semester.
Mandatory assignments must be passed.
Further information in Canvas.
Assessment methods and criteria
Portfolio assessment. Graded, A-F. More information will be given at semesterstart.
The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.