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2025/2026  KAN-CGMAI3003U  Machine Learning for Predictive Analytics in Business

English Title
Machine Learning for Predictive Analytics in Business

Course information
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 Governance, Law, Accounting & Management Analytics
Programme Master of Science (MSc) in Economics and Business Administration - General Management and Analytics (GMA)
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
For academic questions related to the course, please contact course responsible Raghava Rao Mukkamala (rrm.digi@cbs.dk).
Main academic disciplines
  • Finance
  • Information technology
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 03/11/2025

Relevant links
Learning objectives
By the end of this course students will be able to:
  • Understand the fundamental issues and challenges of machine learning.
  • Appropriately choose and appraise machine learning algorithms for predictive analytics in business.
  • Effectively process, summarize, and visualize business data using appropriate analytical tools and methods.
  • Develop and apply machine learning algorithms to address business challenges and support decision-making.
  • Critically assess the ethical and societal implications of applying machine learning in business and societal contexts.
Course prerequisites
A completed bachelor’s degree or equivalent is required. Fundamental mathematics and statistics will be reviewed during the course; however, students are expected to have some prior training in areas such as calculus, linear algebra and probability theory. No programming experience is necessary, as Python will be taught from scratch.
Examination
Machine Learning for Predictive Analytics in Business:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
First re-take: 72-hour home assignment, max. 10 pages. If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Description of the exam procedure

The assessment consists of an assignment comprising several questions on the application of machine learning for predictive analytics in a business context. Students will be provided with a brief and accompanying datasets, and will be required to conduct the analysis using Python. No prior programming experience is required, as the fundamental operations of the software are taught and practised in lectures and workshops. Full submission requirements will be outlined in the briefing.
Course content, structure and pedagogical approach

Machine learning plays an important role in business operations such as fraud detection, sales forecasting, pricing and consumer segmentation. This course introduces business students to its principles and applications, with a focus on predictive analytics. It combines theory and practice, covering essential mathematical and statistical concepts while teaching Python programming from scratch. Each session blends lectures with workshops, and students are expected to bring their laptops for the practical exercises.

   

  • Preliminary assignment: A small assignment
  • Session 1: Introduction and getting started with Python 
  • Session 2: Data manipulation using Python
  • Session 3: Data visualization in Python
  • Session 4: Linear regression
  • Session 5: Logistic regression
  • Midway assignment: A small assignment
  • Session 6: Neural networks
  • Session 7: K-nearest neighbors and naive Bayes 
  • Session 8: Tree-based methods
  • Session 9: Support-vector machines 
  • Session 10: Cluster analysis
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
  • Methodology
  • Models
Research-like activities
  • Analysis
Description of the teaching methods
Teaching comprises face-to-face lectures and workshops for each session. Lectures introduce key concepts, theories and methodologies, while workshops provide hands-on, formative activities. Students are advised to bring their laptops to engage fully in the practical analytics exercises.
Feedback during the teaching period
Formative assessment and feedback will be embedded within session exercises. This interactive approach is designed to consolidate understanding and develop practical skills, ensuring that students are well prepared for the summative assessments.
Student workload
Preliminary assignment 20 hours
Classroom attendance 30 hours
Preparation 129 hours
Feedback activity 7 hours
Examination 20 hours
Further Information
6-week course.
 
Precourse activity: The course coordinator uploads precourse activity 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.

 
Expected literature

Recommended textbooks:

  • Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly, 2012. (Chapters 4-5)

  • Bowei Chen, Gerhard Kling. Business Analytics with Python: Essential Skills for Business Students, Kogan Page, 2025. (Chapters 1-14)

Additional relevant readings:

  • Marc Deisenroth, Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning, Cambridge University Press, 2020. (Chapters 2, 4-6)
  • Kevin Murphy. Machine Learning: A Probabilistic Perspective, MIT Press 2012. (Chapters 1-3, 7-8, 14, 16)
  • Christopher Bishop. Pattern Recognition and Machine Learning, Springer 2007. (Chapters 5, 9)
Last updated on 03/11/2025