Master of Science in Business Analytics
Master of Science in Business Analytics
Program of study:
Master of Science in Business Analytics
Credits:
120
Language of instruction:
English
Study mode:
Full-time
Associate Dean:
Auke Hunneman
Candidate Profile
Learning outcome - Knowledge
Upon completion of the programme, students should have knowledge of:
- Theories and models for decisions making in the private and the public sector.
- Principles of markets, competition and value-creation
- Different data sources and how to convert data into useful information for decision making.
- A wide variety of analytical methods and how they can be used for specific business challenges and their limitations.
- How machine learning can be utilized for predictive analytics.
- The ethical challenges and legal framework regulating data collection, storage, analysis and publication.
Learning outcome - Skills
Upon completion of the programme, students should be able to:
- Identify and formalize unstructured and complex business decisions
- Write a useful code for statistical analyses in a relevant programming language.
- Identify, locate, extract, clean and build databases derived from internal and external data sources
- Analyze relevant data in order to understand a business problem, and conduct relevant predictive analyses.
- Evaluate the value added of analytics projects and communicate the results to management
- Apply and critically assess the results from an empirical study to prescribe optimal solutions for business decisions. Visualize and communicate the proposed solutions.
Learning outcome - General Competence
Students should be able to demonstrate how rigorous analyses can improve the quality of business decisions, and at the same time develop an ability to reflect critically around:
- The limitations of the data and the assumptions of the methods used to analyze data.
- The ethical dilemmas regarding the use of individual-level data, algorithms and statistical tools in general.
- Legal aspects of data analysis with respect to privacy and data security issues.
Course models
Approved in the Programme Committee
Wednesday, September 25, 2024