Curriculum

Programme provision

In order to obtain the MSc degree in Business Analytics the student must fulfil the following requirements:

  • Have passed General Competence Courses adding up to at least 30 points
  • Have passed Technological Specialization Courses adding up to at least 30 points
  • Have performed an MSc thesis of at least 30 points within the field of the general program
  • Have passed a sufficient number of Elective Courses to bring the total number of points of the entire study up to 120.

Curriculum

General Competence Courses

The General Competence Courses ensure that you gain an understanding of the core elements within the degree programme and that you learn to combine technology applications and technological development with economics, management and organization. In addition, you will learn to use your technological expertise in a professional context. You will also learn to describe and indicate solutions to open-ended problems and to work as part of a team where the focus is on interpersonal skills and communication. General Degree Competencies are acquired through courses in the programme's specialized disciplines, and amount to 30 ECTS points.

The following courses are mandatory (counting as 20 ECTS points within the general competencies):

42114 Integer Programming 5 point E7 (Tues 18-22)
or
42137 Optimization using metaheuristics 5 point Spring F2A (Mon 13-17)
or
42112 Mathematical Programming Modelling 5 point January
42576 From Analytics to Action 5 point Spring F1A (Mon 8-12)
42577 Introduction to Business Analytics 5 point Autumn E1A (Mon 8-12)
42578 Advanced Business Analytics 5 point Spring F3B (Fri 13-17)

Choose one among the following courses. The course "Innovation in Engineering" is aimed at students with no knowledge about innovation, while "Facilitating Innovation in Multidisciplinary teams" is for students that have already had experince with innovation. Contact the course responibles if you are in doubt, about which course to select. 

42504 Innovation in Engineering 5 point August
or
42501 Innovation in Engineering 5 point June
or
42500 Innovation in Engineering 5 point January
42505 Facilitating Innovation in Multidisciplinary teams 5 point August
or
42503 Facilitating Innovation in Multidisciplinary teams 5 point June
or
42502 Facilitating Innovation in Multidisciplinary Teams 5 point January

The remaining 5 ECTS within general competencies must be achieved by choosing one of the of the following courses:

02806 Social data analysis and visualization 5 point Spring F3A (Tues 8-12)
or
42417 Simulation in Operations Management 5 point June
or
42186 Model-based machine learning 5 point Spring F4B (Fri 8-12)
or
02807 Computational Tools for Data Science 5 point E7 (Tues 18-22)
or
02456 Deep learning 5 point Autumn E2A (Mon 13-17)

Technological Specialization Courses

Your technological specialization ensures that you acquire the latest technological expertise within your field. Combined with your MSc Thesis and the opportunity to take specialist courses, you will acquire comprehensive academic and practical expertise in your field at a high international level.

 

30 ECTS can be freely chosen among the following list of technical specialization courses, or following one of the suggested study lines:

02110 Algorithms and Data Structures 2 5 point Autumn E2B (Thurs 8-12)
02239 Data Security 7.5 point Autumn E5B (Wed 13-17)
02417 Time Series Analysis 5 point Spring F4B (Fri 8-12)
02425 Diffusions and stochastic differential equations 5 point Autumn E1B (Thurs 13-17)
02427 Advanced Time Series Analysis 10 point Autumn E5 (Wed 8-17)
02431 Risk Management 5 point January
02435 Decision-Making Under Uncertainty 5 point Spring F4A (Tues 13-17)
02443 Stochastic Simulation 5 point June
02456 Deep learning 5 point Autumn E2A (Mon 13-17)
02460 Advanced Machine Learning 5 point Spring F1B (Thurs 13-17)
02582 Computational Data Analysis 5 point Spring F2B (Thurs 8-12)
02805 Social graphs and interactions 10 point Autumn E5 (Wed 8-17)
02806 Social data analysis and visualization 5 point Spring F3A (Tues 8-12)
02807 Computational Tools for Data Science 5 point E7 (Tues 18-22)
42015 Energy Economics 5 point Autumn E2A (Mon 13-17)
42104 Introduction to Financial Engineering 5 point Autumn E5A (Wed 8-12)
42108 Advanced Financial Engineering 5 point Spring F5B (Wed 13-17)
42111 Static and Dynamic Optimization 5 point Autumn E2A (Mon 13-17)
42112 Mathematical Programming Modelling 5 point January
42114 Integer Programming 5 point E7 (Tues 18-22)
42115 Network Optimization 5 point Autumn E4B (Fri 8-12)
42117 Transport Optimization 5 point Autumn E2B (Thurs 8-12)
42136 Large Scale Optimization using Decomposition 5 point Spring F2B (Thurs 8-12)
42137 Optimization using metaheuristics 5 point Spring F2A (Mon 13-17)
42180 Quantitative modelling of behaviour 5 point Spring F3A (Tues 8-12)
42186 Model-based machine learning 5 point Spring F4B (Fri 8-12)
42380 Supply Chain Analytics 5 point Spring F5A (Wed 8-12)
42417 Simulation in Operations Management 5 point June
42430 Project Management 5 point January
or
42429 Project Management 5 point August
42543 Management of organizational change 5 point Spring F4A (Tues 13-17)
42879 Decision Support and Strategic Assessment 5 point Autumn E2B (Thurs 8-12)
46755 Renewables in electricity markets 5 point Spring F2A (Mon 13-17)

Elective Courses

Students can select their remaining points (up to 30 ECTS points) as elective courses from the list of DTU MSc courses.

Any course classified as MSc course in DTU's course base may be taken for credit as an elective course. This includes general competence and technological specialization courses in excess of the minimal requirements. Master students may choose as much as 10 credit points among the bachelor courses at DTU and courses at an equivalent level from other higher institutions.

Head of Studies