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, Introduction to research methods in IS, Basic concepts and theories in IS, Literature reviews and writing style.

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 course will focus on strengthening the theoretical principles of statistics with a focus on practical applications in business settings. The students will also gain experience working with data cleansing, use descriptive statistics to explore data, build simple statistical models, and learn techniques of communicating the 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. The course will also introduce a portfolio of basic data mining techniques necessary for extraction, transformation, cleansing and/or manipulation, analysis of data and communication of statistical results. The course will prepare the students for advanced data analytics courses.

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.

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.

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.