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2021/2022  BA-BEBUV1902U  Enhancing European Competitiveness with Digital Business Analytics

English Title
Enhancing European Competitiveness with Digital Business Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 50
Max. participants 100
Study board
Study Board for BSc in European Business
Course coordinator
  • José Parra-Moyano - Department of Digitalisation
Main academic disciplines
  • Management
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 17-08-2021

Relevant links

Learning objectives
After successfully completing the course, participants will feel comfortable and fluent when working with data to generate knowledge that has a direct, and unequivocal impact on businesses.
  • Demonstrate confidence with the use of business analytics tools, and apply them correctly.
  • Reflect upon and adequately chose the proper business analytics tool for each context.
  • Understand and analyze the managerial implications of the quantitative results of business analytics.
Course prerequisites
Basic knowledge of the programming language R or equivalent.
Enhancing European Competitiveness with Digital Business Analytics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 5 pages
Students will have to solve a case by analysing a dataset in R.
Assignment type Case based assignment
Duration 48 hours to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
The re-exam will have a similar format than the original exam and will be based on the same content taught in class.
Description of the exam procedure

The exam will be a take home assignment that will consist of a short case description and an R file. Students will simulate different datasets in order to answer the questions of the exam. As a consequence, all the students will receive a different exam (no cheating options), while the questions of the exam (and therefore the difficulty level) will be the same for all. The exam is open-book, and students can consult all the sources they need. The exam will consist of an R file in which the students will directly write concise answers. Student will have 2 days (48 hours) to work on the exam.

Course content, structure and pedagogical approach

Business Analytics has a massive impact on any firm throughout all sectors. Therefore, European firms that want to compete in an ecosystem driven by data, need to master the use of digital business analytic tools. In this course we will learn how to use these tools in a European context, focusing on their tangible managerial implications. For example, we will learn how to visualize trade flows between European countries, learn how to assess the efficiency of algorithms, and evaluate if investments in exports technology are worth the money. And all of this, using business analytics.


“Enhancing European Competitiveness with Digital Business Analytics” is designed to introduce participants to key concepts, tools, and practices of business analytics from a managerial perspective. The objective of the course is to provide the students with the analytical tools that will help them to generate insights from data in a robust, correct, and actionable way. The ultimate goal is that the graduates can use the hands-on and knowledge that they will acquire in this course, to enhance the competitiveness of their firms.


Every module contains the introduction of a short theory part that serves as a basis to learn a new tool. Every module continues with a hands-on part, in which the students are given the opportunity to directly apply this new tool to generate insights and useful visualizations from a dataset. Students don’t need to have any previous coding knowledge. All the codes using in class (R Software) will be provided, and carefully explained in class.


Description of the teaching methods
Every session will be clearly structured in three parts: knowledge acquisition, knowledge application, and knowledge incorporation. In the knowledge acquisition part, students will learn a new method/tool/concept. The lecturer will show how to apply this new method/tool/concept and how to generate tangible insights from its application. In the knowledge application part (which will be composed by a short exercise), the students will have to apply the knowledge in an autonomous manner. In the knowledge incorporation part, the lecturer will solve the exercise of the previous sessions for the students, such that they get the solution to their problems and can fill in any gaps they might have.
The first two sessions of the semester will happen (if COVID-19 permits) physically on campus. Afterwards, the sessions will be prerecorded to enable the students to organize their timetables in a flexible manner (self-paced learning).

During the semester, there will be some coffee-classes with 1 teaching assistant that will answer the questions that the students might have while solving the exercises of the knowledge application part. This will also ensure that there is a cohesive and community spirit during the course.

In terms of didactics, the students will receive a slides deck with theory, R scripts with code to conduct the analysis of data, and case descriptions to relate the quantitative results to the context of European competitiveness.

The exercises that the students will receive after each class, are composed of a little case description, a dataset, and a series of questions. Students have to analyse the data and provide answers to the case. Exercises are individuals and meant for self-learning. The exercises are not graded.
Feedback during the teaching period
Students will have the opportunity of solving exercise in physical or digital "coffee classes". The coffee classes will be gatherings in which the students will have the opportunity to ask questions to the TAs in front of the class. TAs will solve the questions live in class, such that all the students can benefit from the answers.
A forum will be offered for the students to ask questions and learn from their peers.

The lecturer will solve the exercises in class after a period of time. This will show the students how to solve the exercises and learn different solution approaches.
Student workload
Lectures of theory 19 hours
Lectures of exercises 19 hours
Preparation of lectures (incl. reading) 48 hours
Conduction of exercises 48 hours
Participation in coffee-class 36 hours
Exam preparation and exam 40 hours
Last updated on 17-08-2021