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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 |
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| 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
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| Teaching methods | |
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| 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.
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| Course prerequisites | ||||||||||||||||||||||||||
| Basic knowledge of the programming language R or equivalent. | ||||||||||||||||||||||||||
| Examination | ||||||||||||||||||||||||||
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| Course content, structure and pedagogical approach | ||||||||||||||||||||||||||
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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.
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| 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. |
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| 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. |
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