MSc in Business Intelligence and Data Analytics

The European Credit Transfer and Accumulation System (ECTS in short) is a tool of the European Higher Education Area for making studies and courses more transparent. It helps students to move between countries and to have their academic qualifications and study periods abroad recognized. The credits below are based on the ECTS.

To graduate, students are required to earn 90 credits as follows:

• 78 credits from taught core courses (including the final project),
• 12 credits either from one of the three concentration tracks or by combining courses from different tracks.

Basic Functional Skills

• BI395 – Foundations of Business Information Technology
• BI405 – Database Management and Cloud Computing
• BI410 – Data Mining, Visualization and Decision Making
• BI415 – Managing Big Data
• BI420 – Python Programming
• BI425 – Information Security Management for Business
• BI130 – Web & Social Media Analytics
• AT600 – Digital Transformation of Business & Organizations
• BI430 – Quantitative Methods & Statistical Analysis
• AT500 – Data Analytics and Artificial Intelligence
• HR495 – Ethics, CSR & Sustainability
• BI500 – Data Science Research Project

Elective Courses

• Operations & Project Management (12/24 ECTS)
• MB675 – Operations & Supply Chain Management
• MB710 – Project Management
• AT800 – Project Management in Information Technology
• AT400 – Blockchain and Applications

Elective Courses

Technical Entrepreneurship & Innovation (12/24 ECTS)

• AT900 – Technical Entrepreneurship
• GD320 – Digital Business Tools and Digital Business Development
• FB415 – Project & Business Financing
• MB750 – Planning & Starting a New Business
  
  

Elective Courses

Financial Services (12/30 ECTS)

• FB470 – Innovative Financial Technologies (FinTech)
• FB500 – Financial Innovation and Financial Regulation
• FB415 – Project & Business Financing
• GD320 – Digital Business Tools and Digital Business Development
• AT400 – Blockchain and Applications

• Demonstrate understanding of the value of (Big) data and data-driven decision making.
• Identify the basic concepts that underpin today’s organizational IT infrastructures, such as concepts of databases, information systems, operations and processes, cloud computing, data warehousing, and enterprise resource planning.
• Apply data mining/analytics (statistical and machine-learning) to solve real-world business problems.
• Develop skills related to data analytics pipeline from collection, processing, analysis and interpretation.
• Effectively communicate to top management the results and implications arising from data analytics, security risk assessments, and emerging technologies.
• Demonstrate professionalism and leadership by taking initiative within their domain of responsibility while working effectively with other team members.