English   Danish

2017/2018  KAN-CINTO1011U  Big Data Management

English Title
Big Data Management

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Last updated on 29-06-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Understand and deploy machine learning techniques including classification, regression and clustering, as well as data visualisation.
  • Demonstrate an analytical understanding of data science methods, including the testing and assessment of data models.
  • Demonstrate an understanding of business and societal issues in the application of data science, including the expected value or profit for a given model.
Course prerequisites
Students should have the ability to work with computational models and quantitative methods.
Big Data Management:
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.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 20 pages
Assignment type Project
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
Preparation time No preparation
Grading scale 7-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

This course is designed to equip students with practical knowledge of tools and techniques for the purpose of analyzing large data sets. Students will also be exposed to different ways by which organizations leverage these tools and techniques to develop effective data management strategies for innovation and value creation. The course will further explore the transformative potential of big data management in different sectors of business and society.


The course has a blended format, with most lectures presented online, together with associated online activities. In addition, there will be weekly hands-on lab sessions. The course includes an independently chosen project on big data management. The project will take the form of a business case analysis. Students will select a dataset, to which they apply data science techniques, building relevant models and assessing them from a business and data science perspective.


The course will cover the following main topic areas:

  • Machine Learning tools and techniques, including classification, regression, and clustering
  • Value creation through big data management
  • Model analysis and data visualization
  • Transformative potential of big data analytics across various sectors, such as healthcare, government and retail 


Teaching methods
A mixture of online lectures, other online activities such as quizzes, group work, and practical exercises in a hands-on session
Feedback during the teaching period
Weekly feedback on hands-on exercise, feedback on workplan for final project.
Student workload
Lectures 30 hours
Class Preparation 116 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the literature on LEARN before they buy the books.


Provost, F. and Fawcett, T. Data Science for Business: What you need to know about Data Mining and Data-Analytic Thinking, 2013

Last updated on 29-06-2017