Course Catalogue

The aim of the course is to empower the participants to integrate modern interpretation of analytical techniques, theory, and methodology in the analyses of socio-economic problems related to their research needs. Applied analytics is a way to understand a variety of processes in business, strategy, and management. The importance of analytical skills for PhD research in business has been on the rise. PhD students, whose research is focused on business, strategy, and management, are required to be fully equipped with sufficient knowledge and analytical "toolboxes" to be successful in their doctoral studies and future career.

Most of the course revolves around developing fundamental analytical skills. The format combines lectures with in-class discussions. The PhD student will learn and systemize skills in programming required for analytics gently covering the R-fundamentals with a very smooth and comprehensive transition to the methods required for research that are easy and fast to master. During the course, R is our "weapon of choice" as it is an easy-to-use, flexible and popular language that is used in many business schools and research institutions around the world. This course covers the most fundamental programming topics necessary for their research needs. In doing so, the PhD student will be introduced to many features of the R-language that are often omitted from more basic training. During the course, students will master the language constructs, data types and structures, and functions. In addition to theory, practical tasks are included where students develop knowledge and hone analytical skills in R. After successful completion, students will be able to use the experience gained in this course as a foundation for their further development of analytical and research skills.

The course concludes with an exploration of the fundamental theoretical principles of data analytics, equipping PhD students with a solid understanding of data-driven approaches in business and management. The final sessions will offer a theoretical review of key concepts, including the understanding of structured and unstructured data and the foundational aspects of big data. Additionally, students will be introduced to the fundamental principles of machine learning and artificial intelligence (AI), along with their applications in business. These insights will enable students to critically assess analytical paradigms and effectively apply data-driven skills to analyze collaboration and interdependencies within organizations and the broader business environment.

The course will be given entirely online (via Canvas, Zoom, and other digital tools), with no in-classroom teaching or on-campus activities.

Course materials for self-study will be available on Canvas a few weeks before the course begins, and students are expected to review them in advance.

Visit our website for more information (see course details).