Master of Science in Information Systems - Business Analytics

The course provides an introduction to the IS research field. Students will gain advanced knowledge of the key concepts and theories of IS research. They will acquire specialised problem-solving skills, being able to analyse and synthesize a research case. They shall take responsibility to a literature review of a specific IS topic.

Central topics include Information Systems as a research field, IS development, IS innovation, IS as sociotechnical and complex systems, Analytics and data driven decision making, Introduction to research methods in IS, Basic concepts and theories in IS, Literature reviews and writing style.

Data Management is an essential and critical process with organizations. This course will provide students with an introduction to data management techniques, data warehousing and business intelligence. Students will gain advanced knowledge of key theories and concepts of data management processes, data collection, sharing and storage, data documentation, data compliance, data preservation, and an overview of the open data requirements. They will acquire specialized problem-solving skills, being able to analyze and prepare data management plans.

Data and visual analytics are an evolving field concerned with analyzing, modeling, and visualizing complex high-dimensional data. This course will introduce students to the data visualization domain by covering state-of-the-art modeling, analysis and advanced visualization techniques. It will emphasize practical challenges involving complex real-world data and include real-world case studies and hands-on work with several leading visual analytics tools and programming languages. Students will gain advanced knowledge of the art of decision-making, as well as acquire specialized problem-solving skills and deliver value to organizations through the development of advanced visualizations.

The main aim of this course is to provide students with the fundamental knowledge of ethics and sustainability necessary for responsible innovation and the development of new information technologies (IT) in the modern society. The central topics include the role of ethics in responsible innovation and IT development; information privacy concerns, social, economic, and environmental impacts of innovations and new ITs; and how IT development and innovation can contribute to achieving the UN Sustainable Development Goals. In covering ethical and sustainability issues, the course addresses the perspectives of various stakeholders at the individual level (IT developers, innovators, consumers, investors), the organizational level (commercial, public, and non-governmental organizations), and the societal level (local and regional communities, nations, international society). Group work on possible solutions to real-life ethical and sustainability challenges constitutes an essential part of the course.

The course will provide students with a comprehensive understanding of fundamental statistical theories and their practical applications in business settings, along with an introduction to essential machine learning techniques. The students will also gain practical skills to analyze data, build statistical models, work with machine learning algorithms, and effectively communicate results. Topics include but are not limited to: data transformations, descriptive statistics reporting, Central Limit Theorem, elements of a statistical test, Type I and Type II errors, p-value, correlation, and dimensionality reduction as well as supervised and unsupervised machine learning techniques. The course will prepare the students for advanced data analytics courses.

This course introduces students to machine learning algorithms and techniques to deliver value for organizations. Students will gain advanced knowledge of key theories, frameworks, and concepts of data analytics and machine learning. They will acquire specialized analytical skills to develop data analytics projects to support decision-making and solve real-world business problems. Upon completion of this course, the students should understand the fundamental challenges of machine learning such as model assessment, selection, complexity, etc. and be able to implement machine learning using open source machine learning libraries.

This course is intended as a master level introduction to research methodology and the research process. This introduction gives the students an overview of the basic concept, methods, and practice of research.

Research is a cyclical process where new and carefully planned investigations build and extend on established work. The aim is to provide students with a fundamental understanding of research as a conceptual, empirical, and practical approach to gathering new insight and knowledge. The content provides a broad overview of how researchers work within the fields of economy, innovation, and technology. It presents students with relevant methods from these domains, along with their possibilities and limitations.

Students will learn a systematic approach to empirical investigation, including literature search, research design and methodology, qualitative and quantitative analyses, and the presentation and evaluation of results.

At completion of the course, students will be able to study and interpret existing research on a topic and suggest approaches to broaden or deepen knowledge within a given topic.

The overall objective of this course is to help students conceptualize and prepare a timely and relevant research proposal, and to nurture a sense of inquisitiveness and active participation in research. The course aims at offering insight into the process behind a successful research project. It has an applied approach that involves collaborative and reciprocal partnerships between the university (faculty, staff, and/or students) and external communities for the mutually beneficial exchange of knowledge and resources. The research proposal forms the basis for the master thesis and the allocation of supervisor(s).