MET529 Applied Business Analytics

• 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

  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

  The course format: Digital lectures. In Fall 2024, the course is taught intensively on the 11 th , 13th, and 15th of November 2024. It is based on online teaching (Canvas, Zoom, and/or other digital tools) with no in-classroom teaching and with no on-campus activities.

• Restricted access

  1. The course is open to:

  • PhD candidates at NHH.
  • PhD candidates at Norwegian institutions.
  • PhD candidates at other international institutions.
  • PhD candidates from the ENGAGE.EU alliance.
  • Motivated master's students at  NHH  may be admitted after application but are subject to approval from the course responsible on a case-by-case basis.
  • Individuals outside academia may be admitted after application, but are subject to the approval from the course responsible and the Vice Rector for Research on a case by case basis.

  2. All external (non-NHH) students, please follow the link for more details:

  https://www.nhh.no/en/study-programmes/phd-programme-at-nhh/phd-courses/become-a-visiting-student-at-a-phd-course-at-nhh/

  3. The course capacity limit: 20 participants.

  4. For more questions:

  For any questions regarding the course registration, please contact the NHH Section for Doctoral Education (email: phd@nhh.no):

• Recommended prerequisites

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

• 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 of to 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.

  Re-take is offered the semester after the course was offered for students who signed up for the initial evaluation.

• Grading Scale

  Pass-Fail

• Computer tools

  Participants will need software R and RStudio.

• 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