Business Intelligence

About the course

Emner

Markedsføring Forsyningskæde Logistik Ledelse

Course content

Business intelligence refers to the technologies, applications and practices used to collect, integrate, analyse and present business data in support of decision-making. This course develops students’ analytical skills for business decision-making and evaluation, combining lectures and workshops with a strong emphasis on practical application. Students make extensive use of R and its widely adopted packages, including tidyverse, tidymodels and tidytext.

Preliminary assignment: A small assignment
Session 1: Introduction
Session 2: Understanding business data
Session 3: Descriptive analytics
Session 4: Efficient data manipulation in R
Session 5: Regression methods
Midway assignment: A small assignment
Session 6: Classification methods
Session 7: Data pre-processing, model training and evaluation
Session 8: Cluster analysis
Session 9: Text processing with R
Session 10: Sentiment analysis and topic modelling

See course description in course catalogue

What you will learn

By the end of this course students will be able to:

Explain the key concepts of business intelligence and the role of data-driven decision-making in business and society.
Identify and critically assess different types of analytics, data sources, and formats relevant for business challenges.
Process, summarize, and visualize business data using appropriate analytical tools and methods, while communicating insights effectively.
Apply a wide range of quantitative and machine learning methods to address complex business problems, balancing competitiveness with responsibility.
Evaluate and select suitable analytical approaches for specific business contexts.
Critically assess the ethical and societal implications of applying business intelligence in business and societal contexts.

Course prerequisites

This course is designed for graduate students in business disciplines such as information systems, marketing, operations management and analytics. A completed bachelor’s degree or equivalent is required. No prior knowledge of mathematics or programming is necessary, although students with basic mathematical or analytical skills are preferred.

Facts

Level

Kandidat

Type

Valgfag

ECTS

7,5

Code

CGMAI3002U

Teaching periods

Sommer

Academic year

2025/2026

Status

Available places

Teaching language

English

Teaching Format

På campus

Exams

Skriftlig opgave
Individual exam, sommer

Assessment type

7-trins skala