Applied Analytics in Strategy and Management

MET529 Applied Analytics in Strategy and Management

Autumn 2020

  • 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 the required 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 the fundamental theoretical principles of data-driven analytics and network analysis. First, the participants will be exposed to, and discuss, a variety of conceptual and theoretical perspectives on the study of data-driven approaches in business and management, along with methods utilized in the theoretical frameworks. Based on this session, the PhD student obtains a clear understanding of the evaluation of data analytics based on the range of theoretical topics such as interpretation of structured and unstructured data, and some fundamental principles of big data and machine learning/artificial intelligence. Here we also discuss the origin of new analytical paradigms.  Second, we will cover some theoretical background on social and economic networks, and make an overview of concepts used to describe and measure networks. It will help students to build an effective practice-oriented knowledge to explore collaborations and interdependencies both within organizations as well as with others in the world of work that people/organizations operate in.

  • Learning outcome

    With the growth and increased access to large-scale and complex socio-economic information, Applied Analytics gravitates towards the core of methods to understand processes in business, strategy, and management. The purpose of this course is to offer the student insights within Applied Analytics and develop practical skills (using R-based programming concepts) and knowledge about contemporary methods and techniques. It targets PhD students who wish to develop (or systemize) their skills in Applied Analytics for better performance in research.

    By the end of this course, the students will be 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

    The course is taught intensively. Teaching will consist of lectures, in-class discussions, and in-class exercises.

  • Required prerequisites

    No previous knowledge and skills in analytics and computer programming are required

  • Assessment

    Written term paper. Students may work on the written term paper individually or collaborate in groups. The term paper must be written in English. 

  • Grading Scale

    Pass-Fail

  • Computer tools

    Participants should have access to a computer connected to the Internet.

  • Literature

    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

Overview

ECTS Credits
2.5
Teaching language
English
Semester

Autumn. Offered autumn 2020.

Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.

Course responsible

Associate Professor Ivan Belik, Department of Strategy and Management.