Topics
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.
Learning Outcome
By the end of this course, the students are able to…
Knowledge
- understand the fundamentals of programming and analytical concepts
- explain principles and evaluation of applied analytics
- interpret data-driven mechanisms and structures
Skills
- analyze different types of programming-based problems and their solutions
- use fundamental analytical tools and adapt them to the characteristics of specific tasks
- apply fundamental programming skills to research
- develop basic analytical and modeling solutions
General Competence
- evaluate fundamental programming principles and tools in applied analytics
- interpret analytical models and frameworks
- employ fundamental analytical knowledge required for research in business, strategy, and management
- discuss what data-driven approach means
Teaching
Course format: Digital lectures. In autumn 2026, the course is planned to be taught intensively during the period November 9–13, 2026. It will be delivered 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.
Assesment
Written term paper.
Students may work on the written term paper individually or collaborate in groups of 2-3 persons. The term paper must be written in English. All details regarding the term paper will be posted on Canvas at the start of the course. The submission deadline is 12:00 (noon) on the date that is approximately three weeks after the final lecture.
Grading
Pass/Fail
Computer Tools
Participants will need software R and RStudio.
Litterature
Venables, W. N., & Smith, D. M. “An introduction to R”
An Introduction to R (edited by the R Development Core Team):
https://cran.r-project.org/doc/manuals/r-release/R-intro.html
Credits
5
COURSE RESPONSIBLE
Professor Ivan Belik, Department of Strategy and Management.