This course is designed to provide an introduction to theoretical perspectives and foundations in the fundamental topics of information and communication technologies, digital systems and digitization in business processes. This course aims to help information systems postgraduate students to carry out a research in Information Systems field as well as in fields of business, information science, communications, computer science, etc. In this course, students will be able to apply and understand the strengths and weaknesses of the commonly used research methodologies in information systems, enhance their ability to theorize about information technology and learn the process of publishing information technology related research in information systems and non-IS journals.

This course introduces the main concepts of electronic government (e-government) practices and analyses the practical implications of e-government for public organizations. The course examines the development of e-government practices, discusses and analyses main forms of e-government methods, focuses on the measurements of e-government. E-participation and e-engagement will also be examined throughout the course. Furthermore, students will also learn wider concepts such as barriers and impediments towards e-government and will be able to evaluate solution methods based on real case scenarios. The impact of innovation and technological infrastructure will also be examined within the course. After the successful completion of the course students will have an in-depth understanding of the e-government practices which are used worldwide as well as impacts on organizational effectiveness, transparency and accountability.

The aim of the course is to provide students with a basic concepts related to organizational behavior and show them how individual and group behavior is important for the management of an organization.

This course provides a comprehensive definition of “Big Data” and the machine learning approaches for managing and processing them. With storage and computational power getting significantly cheaper and faster, big data sets are increasingly available and the need for machine learning approaches for handling big data becomes more significant. In this course, big data harvesting and manipulating methods, supervised and unsupervised machine learning techniques (especially artificial neural network), text data analysis and cloud computing are covered. By completion of this course, students will gain the ability to harvest big data from the web and process them by using supervised and unsupervised neural networks.

This course is designed to examine the internal and external communication systems of business organizations. It is also examined that the uses of communication media such as internet and intranet in organizations.

This course is intended to explain the state art of the expert system technology. Based on the main concepts, students will learn the components of expert systems. The students will learn knowledge acquisition and validation. Moreover, they will learn different techniques of knowledge representation and the related programming languages and tools. Students will learn also the inference processes, explanation and reasoning under uncertainty. The course examines the importance of expert systems as management information systems and its role in management. After the successful completion of the course students will be able to discuss and use the expert system in different aspects of the organization and evaluate its impact on the organizational effectiveness.

This course aimed to analyse the forming and designing the planned message and its communication techniques and media within the framework of practices in strategic communication management. Relating to this, the case studies on how the effective media management should be are examined.