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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
  • Marin Jovanovic - Department of Operations Management (OM)
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 06-05-2025

Relevant links

Learning objectives
By the end of the course, students will be able to:
  • Create business analytic models to identify relationships relevant to specific business problems and generalize the application for business decision-making.
  • Explain important issues and apply analytical methods that are appropriate for the problem at hand.
  • Conduct business analytics by collecting, assessing, and applying business performance data.
  • Solve a business problem by using analytic methods to generate relevant insights.
  • Communicate the findings of the analysis and evaluate limitations and alternative methods.
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
Business Analytics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Aids Limited aids, see the list below:
The student is allowed to bring
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

The exam consists of a series of questions and exercises that require analysis and problem-solving using the methods and approaches covered in the course. Responses must be grounded in the techniques and methodologies practiced during course exercises. Active participation in these exercises is the most effective preparation for the exam.

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:

  • Introduction to Business Analytics – Overview of the field and its applications in modern business environments.
  • Data Management and Visualization – Techniques for data handling, cleaning, and visualization.
  • Statistical Foundations – Probability theory, statistical inference, and hypothesis testing.
  • Regression Analysis – Linear and multiple regression models for predictive analytics.
  • Time Series Analysis and Forecasting – Methods for analyzing and predicting temporal trends.
  • Supervised Learning – Fundamental and advanced machine learning techniques, including KNN, naïve Bayes, support vector machines, decision trees, and random forests.
  • Deep Learning – Introduction to gradient boosting models (e.g., XGBoost) and deep learning architectures.
  • Data Quality and Integrity – Examination of data reliability and its impact on analytical outcomes.

 

A strong emphasis is placed on hands-on learning. Students will engage in:

  • Data collection, structuring, analysis, and visualization using industry-standard tools.
  • Case studies that apply business analytics techniques to real-world managerial problems.
  • Hands-on exercises and group project presentations that reinforce theoretical knowledge through practical implementation.
  • Industry expert sessions, offering insights into business analytics applications across various sectors.

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
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
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
Lectures 33 hours
Preparations before lectures including a group assignment 83 hours
Exam preparations 90 hours
Expected literature

A full list of relevant literature will be provided in class. The following works are indicative:

  • Jaggia, S., Kelly, A., Lertwachara, K., & Chen, L. (2023). Business Analytics ISE, Second Edition. ISBN: 9781265087685.
  • Chollet, F., Kalinowski, T., Allaire, J. J. (2022). Deep Learning with R, Second Edition. Manning Publications. ISBN: 9781633439849
  • Boehmke, B., & Greenwell, B. M. (2019). Hands-On Machine Learning with R (1st ed.). Chapman and Hall/CRC. https:/​/​bradleyboehmke.github.io/​HOML/​
Last updated on 06-05-2025