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2026/2027  KAN-CDSCV1900U  Big Data Analytics

English Title
Big Data Analytics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 100
Study board
Study Board for Digitalisation, Technology and Communication
Programme Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Travis Greene - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 19-01-2026

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Characterize the phenomena of big data analytics and predictive modelling and apply relevant concepts, methods, and tools for analysing big data in real-world organizational/societal contexts.
  • Understand the linkages between business intelligence/analytics and the potential costs and benefits for organisations and relevant stakeholders.
  • Formulate a compelling and realistic use case for the applicability of various analytical techniques, evaluation methods, and algorithms on big/organizational/social/open datasets to improve decision-making and resource allocation.
  • Concisely present and evaluate the findings of a big data analytics project in terms of stated project goals and reflect on the project solution's potential impact on organisations and relevant stakeholders.
  • Create and accurately interpret relevant and useful data visualizations for predictive modelling contexts.
  • Display nuanced, critical awareness of the impact of data science project pipeline choices and employ writing skills appropriate for academic standards in the written report.
Course prerequisites
This course is a part of the minor in Data in Business.
The course takes a practical and hands-on approach to data science in organizational and societal settings. It may not be suitable for students interested in the mathematical foundations of predictive modelling. Moreover, this is a fast-paced and intensive course comprising visual, predictive, and text analytics modules. Students are expected to have some prior background in quantitative methods and basic statistics to follow the analytical techniques and algorithms taught in the course. Students without prior R coding experience may need supplemental practice to get up to speed.
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): 2
Compulsory home assignments
Each student must have 2 out of 3 activities approved in order to qualify for the final exam.


The activities are three group reports of max. 5 pages written in groups of 2-4 students. Each report forms the foundation of a part of the final report. The reports are designed to help students find and analyze a relevant dataset for their analytics project and understand the expectations of the final project before submission.

There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate in the compulsory activities due to documented illness, or if a student does not have the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam: one home assignment (max.10 pages) which will cover 2 mandatory activities.
Examination
Big Data Analytics:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-4
Size of written product Max. 30 pages
The student can also choose to have an individual exam. The size of the written product is 15 pages for an individual exam.
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
The students can choose to hand in the same project, or a new or revised project.
Description of the exam procedure

The final exam is a group oral exam based on a group written product of 2-4 students (maximum 30 pages). The written product is based on a group project geared towards a business or social application of big data analytics (i.e., machine learning) building on the topics and techniques covered in class. In the written product, students are expected to clearly motivate the project idea, articulate the current state of research on the domain/topic, derive potential research questions needed to advance knowledge in the area, evaluate the performance of relevant solutions, and discuss how the solution could be deployed in a real-world organizational setting.
Course content, structure and pedagogical approach

This course equips students with knowledge of key concepts, methods, techniques, and tools of big data analytics from a business and societal perspective. The course covers how to find interesting real-world datasets and how to manipulate, transform, analyze, visualize and report big data in order to create business value. The course focuses on using big data for predictive rather than explanatory modeling purposes with the goal of improving organizational decision-making and (automated) allocation of resources. Course topics are listed below:

 

  • Big Data Foundations: data science concepts, organizational opportunities, and evolving legal and ethical challenges of big data
  • Data: Data types and tabular formats, observational vs. experimental data, and data cleaning and pre-processing pipelines
  • Machine Learning: statistical fundamentals of AI and machine learning with a focus on interpretable supervised machine learning methods
  • Visual Analytics: data visualization techniques for exploration and reporting 
  • Text Analytics: document classification 
  • Time series forecasting: regression-based forecasting 

 

The course uses R and R Studio for all activities. This software is available for free.

   
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
  • Classic and basic theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Activities that contribute to new or existing research projects
Description of the teaching methods
Lectures
Exercises
Demos
Tutorials
Cases
Feedback during the teaching period
As part of the mandatory assignments, the students will have to prepare reports (max. 5 pages) written in a group of 2-4 students. Each group will be provided with written feedback on the mandatory assignments in addition to general class feedback. Personalized feedback is provided during the weekly hands-on exercises in the classroom.

Student workload
Lectures 33 hours
Workshops 22 hours
Self study 48 hours
E-learning 23 hours
Project Work 50 hours
Project Report 30 hours
Total Hours 206 hours
Expected literature


The expected literature might change before the semester starts. Students are advised to find the final literature on Canvas before the start of the class.

 

LECTURE NOTES

 

Lecture notes for each week are posted on Canvas. The lecture notes cover textbook course material in a more conversational tone and include relevant R code for the weekly exercise sessions. Students are therefore advised to read and engage with the lecture notes prior to each exercise course. 

 

If you want to dig deeper into the topics covered, please see the resources below.

 

FURTHER READING

 

Books:

 

  • Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press.
     
  • Martens, D. (2022). Data science ethics: Concepts, techniques, and cautionary tales. Oxford University Press.
     
  • Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.

 

  • Hyndman, RJ, & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts: https:/​/​www.otexts.org/​fpp/​
     
  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.

 

 

Research Papers:

 

  • Guidolin, M., & Scarpa, B. (2025). Data Science in the Kitchen. Harvard Data Science Review, 7(4). 
     
  • Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37-45.
     
  • d'Alessandro, B., O'Neil, C., & LaGatta, T. (2017). Conscientious classification: A data scientist's guide to discrimination-aware classification. Big data, 5(2), 120-134.
     
  • Wickham, H. (2014). Tidy data. Journal of statistical software, 59, 1-23.
     
  • Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.
     
  • Shmueli, G. (2010). To explain or to predict?. Statistical science, 289-310.

 

 

 

 

 

 

 
Last updated on 19-01-2026