2025/2026 KAN-CGMAO2003U Business Analytics
English Title | |
Business Analytics |
Course information |
|
Language | English |
Course ECTS | 7.5 ECTS |
Type | Mandatory |
Level | Full Degree Master |
Duration | One Semester |
Start time of the course | Spring |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Study board |
Study Board for Governance, Law, Accounting & Management
Analytics
|
Course coordinator | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 06-05-2025 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||
By the end of the course, students will be able
to:
|
||||||||||||||||||||||||
Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||||
Number of compulsory
activities which must be approved (see section 13 of the Programme
Regulations): 1
Compulsory home
assignments
The student must obtain approval for 1 out of 2 possible group assignments in order to attend the ordinary exam. The assignment can be completed in groups of 4 or 5 students. Students will have no extra opportunities to get the required number of compulsory activities approved prior to the regular exam. If a student has not received approval for the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam. If, prior to the retake exam, a student has still not been approved for the required number of compulsory activities but otherwise meets the preconditions set out in the program regulations, completing an extra assignment instead is possible. The extra assignment is a 10-page home assignment that covers the required number of compulsory activities. If the completed assignment is approved, the student will be able to attend the retake. |
||||||||||||||||||||||||
Examination | ||||||||||||||||||||||||
|
||||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||||
Business Analytics is an interdisciplinary field that leverages data, statistical models, and predictive algorithms to analyze business dynamics and support data-driven decision-making. This course equips students with the analytical competencies necessary to address managerial challenges, interpret complex datasets, and derive actionable insights within a practical business context. The curriculum is designed to develop proficiency in key business analytics techniques. Students will gain expertise in data manipulation, visualization, statistical inference, hypothesis testing, regression analysis, time series forecasting, and machine learning methods. The course also introduces supervised learning techniques, including k-nearest neighbors (KNN), naïve Bayes, decision trees, and support vector machines, alongside an introduction to deep learning models and eXtreme gradient boosting (XGBoost).
The course follows a structured progression, starting with fundamental concepts and advancing to complex analytical methods. The core topics include:
A strong emphasis is placed on hands-on learning. Students will engage in:
Students are expected to actively participate in all course components. For session schedules and locations, please refer to the CBS Calendar. |
||||||||||||||||||||||||
Research-based teaching | ||||||||||||||||||||||||
CBS’ programmes and teaching are research-based. The following
types of research-based knowledge and research-like activities are
included in this course:
Research-based knowledge
Research-like activities
|
||||||||||||||||||||||||
Description of the teaching methods | ||||||||||||||||||||||||
This course combines theoretical foundations with
hands-on experience to equip students with essential business
analytics skills. Lectures provide a structured exploration of
business problems, grounding them in relevant theoretical and
statistical contexts. These are reinforced through exercises that
develop practical competencies, including data manipulation,
exploration, modeling, and the clear communication of analytical
insights. Students will extensively use R Studio, an
industry-standard tool, ensuring they gain relevant and
transferable skills. To enhance flexibility, pre-recorded exercise
tutorials will be available on Canvas for additional support.
Adopting a process-oriented approach, the course frames business analytics challenges as opportunities for both conceptual understanding and practical problem-solving. It follows the Tidyverse workflow—encompassing key steps such as import, tidy, transform, visualize, model, and communicate—to streamline data analysis using a suite of specialized R packages. This methodology emphasizes the development of efficient, readable, and reproducible code, a crucial skill for tackling real-world data science challenges. To deepen learning and engagement, mandatory group projects encourage students to analyze diverse problems, datasets, and analytical techniques. These projects foster collaboration and critical thinking, ensuring that students not only grasp theoretical concepts but also apply them effectively in meaningful, data-driven solutions. |
||||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||||
Feedback in this course is delivered through
three key channels:
1. Interactive feedback during lectures and exercises Students have the opportunity to engage directly with the instructor during lectures and exercises, receiving immediate clarification and guidance on any questions that arise. 2. Expert feedback in the fourth exercise session During the fourth exercise session, students present their mandatory group projects and receive constructive feedback from both faculty members and external business analytics experts. This session provides valuable industry and academic perspectives to refine their analytical approaches. 3. Summative written feedback on final group projects Upon submission of the final group project, students receive detailed written feedback, offering insights into their analytical process, interpretation of results, and overall presentation. Active participation in lectures, workshops, and exercises is expected. For additional personalized feedback, students are encouraged to make use of office hours for one-on-one discussions with the instructor. |
||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||
|
||||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||||
A full list of relevant literature will be provided in class. The following works are indicative:
|