2024/2025 KAN-CCMVI2071U Business Intelligence
English Title | |
Business Intelligence |
Course information |
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Language | English |
Course ECTS | 7.5 ECTS |
Type | Elective |
Level | Full Degree Master |
Duration | Summer |
Start time of the course | Summer |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Min. participants | 30 |
Max. participants | 60 |
Study board |
Study Board for cand.merc. and GMA (CM)
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Course coordinator | |
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For academic questions related to the course, please contact course responsible Raghava Rao Mukkamala (rrm.digi@cbs.dk). | |
Main academic disciplines | |
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Teaching methods | |
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Last updated on 07/11/2024 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||
By the end of this course students will be able
to:
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Course prerequisites | ||||||||||||||||||||||||||
This is a course for graduate students in business subjects (e.g., information systems, marketing, operations management, business analytics). No mathematics and programming knowledge and skills are needed for entering this course. However, students with basic mathematical or analytical skills are preferred. | ||||||||||||||||||||||||||
Examination | ||||||||||||||||||||||||||
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||||
Business intelligence refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business data in order to support business decision making. Essentially, it is a collection of data-driven decision support models. This course teaches students analytical skills on data to support decision making and evaluation in business. It uses a combination of lectures and workshops. The course emphasizes the practical applications and makes extensive use of R for intelligent business analytics.
Preliminary assignment: A small assignment (with several questions) Session 1 Introduction Session 2 Understanding business data Session 3 Descriptive analytics Session 4 Efficient data manipulation in R Session 5 Regression methods (for sales or price forecasting, etc.) Feedback activity: A small assignment (with several questions) Session 6 Classification methods (for fraud detection, customer engagement analysis, consumer segmentation, etc.) Session 7 Data pre-processing, model training and evaluation Session 8 Cluster analysis (for consumer segmentation, etc.) Session 9 Text processing with R (e.g., string operations, regular expression) Session 10 Sentiment analysis and topic modelling |
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Description of the teaching methods | ||||||||||||||||||||||||||
The teaching methods involve face-to-face instruction in the classroom, and students are advised to bring their laptops for the purpose of conducting practical analytics exercises. | ||||||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||||||
Student survey feedback. | ||||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||||
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Further Information | ||||||||||||||||||||||||||
6-week course.
Preliminary Assignment: The course coordinator uploads Preliminary Assignment on Canvas at the end of May. It is expected that students participate as it will be included in the final exam, but the assignment is without independent assessment and grading.
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Expected literature | ||||||||||||||||||||||||||
Recommended textbooks:
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