Business Analytics and Data Science

Course overview

• The minor consists of 30 EC in total and is a choice of 5 courses from the list below. Your choice should satisfy the following constraints:

  Choose 2 courses in period 1,
  Choose 2 courses in period 2,
  Out of the courses Data Structures and Algorithms for AI (period 1) and Data Structures and Algorithms for CS (period 2), you can choose at most one.

  Students enrolled in the BSc AI and BSc CS programs are not permitted to count the courses Data Science and Algorithms for CS/AI towards this minor.

  They have to choose one of these courses:

  • Introduction to Reinforcement Learning (from the minor Situated AI)
  • Robotics (from the minor Situated AI)
  • Advanced Network Programming (from the minor Deep Programming)
  • Equational Programming (from the minor Deep Programming)
  • Calculus 1 (from the Bachelor Business Analytics)

• Strategic Management of Technology and Innovation (5 out of 7)

  In this course students learn to critically evaluate innovation management concepts from academic literature and popular management press.

• Data Structures and Algorithms for AI (5 out of 7, at most 1 out of DSA for AI/CS)

  After taking this course, you have basic knowledge of data structures and algorithms, know that different data structures are suitable for different computational problems, and are able to analyze the performance of algorithms, with regard to their time and space requirements.

  The courses Data Structures and Algorithms for AI and Data Structures and Algorithms for CS are equivalent to each other in content and level. Thus at most one out of these two courses is allowed in the minor Business Analytics and Data Science.

  Students enrolled in the BSc AI and BSc CS programs are not permitted to count the courses Data Science and Algorithms for CS/AI towards this minor.

  They have to choose one of these courses:

  • Introduction to Reinforcement Learning (from the minor Situated AI)
  • Robotics (from the minor Situated AI)
  • Advanced Network Programming (from the minor Deep Programming)
  • Equational Programming (from the minor Deep Programming)
  • Calculus 1 (from the Bachelor Business Analytics)

• Data Analytics and Privacy (5 out of 7)

  Data Analytics and Privacy focuses on the role of European fundamental rights and legal principles in the regulation of data analytics, with a general focus on the right to privacy and data protection.

• Data Structures and Algorithms for CS (5 out of 7, at most 1 out of DSA for AI/CS)

  The goal of the course is to get acquainted with basic data structures and the the design and analysis of algorithms.

  The courses Data Structures and Algorithms for AI and Data Structures and Algorithms for CS are equivalent to each other in content and level. Thus at most one out of these two courses is allowed in the minor Business Analytics and Data Science.

  Students enrolled in the BSc AI and BSc CS programs are not permitted to count the courses Data Science and Algorithms for CS/AI towards this minor.

  They have to choose one of these courses:

  • Introduction to Reinforcement Learning (from the minor Situated AI)
  • Robotics (from the minor Situated AI)
  • Advanced Network Programming (from the minor Deep Programming)
  • Equational Programming (from the minor Deep Programming)
  • Calculus 1 (from the Bachelor Business Analytics)

• Information Retrieval (5 out of 7)

  This course covers the core aspects of information retrieval and search engines, including indexing, Boolean retrieval, the different types of queries, query execution, the vector space model, web crawling, networks, link analysis, PageRank, classification, and clustering.

• Logistics Analysis (5 out of 7)

  The overall objective of the course Logistics Analysis is to furnish you with knowledge and skills to analyze logistics problems in a variety of industries including manufacturing, transportation and services.

• Data Wrangling (5 out of 7)

  Data wrangling is the process of gathering data in its raw form and molding it into a form that is suitable for its end use. This course is about how to gather the data that is available and produce an output that is ready to be used.