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MSc in Business Administration and Data Science

Pro­gram­ming, Al­gorithms and Data Struc­tures

About the course

Topics
IT

Course content

This course covers three core areas:

  1. Python Programming: Fundamentals, functions, classes, modules, data structures, and collections with emphasis on writing readable, modular, and maintainable code using best practices. Includes hands-on use of key libraries.
  2. Object-Oriented Design: Core concepts (including classes and objects, inheritance, encapsulation, and polymorphism) applied to structuring robust and reusable Python programs.
  3. Algorithmic Analysis: Algorithm design principles and computational complexity, with a focus on evaluating efficiency and selecting appropriate strategies for large or complex datasets.

 

Through lectures and hands-on exercises, students gain practical experience applying these concepts. By the end of the course, students will be able to confidently use Python and relevant libraries to solve problems. 

 

The use of Generative AI tools or applications is not permitted during written sit-in exams or in any mandatory home assignments for this course.

See the full course description in the course catalogue

What you learn

Students should meet the following learning objectives:

  • Apply core Python programming concepts (including procedural, object-oriented, and functional paradigms) to formulate and solve problems.
  • Design and implement well-structured Python programs that follow best practices in readability, modularity, and maintainability appropriate for application workflows.
  • Use key Python libraries (NumPy, pandas, Matplotlib/Seaborn, and similar tools) to load, manipulate and analyze structured datasets.
  • Assess the computational efficiency of common data processing operations and select appropriate algorithmic strategies when working with large or complex datasets.
  • Evaluate and justify programming and analytical choices (such as data structures, library selection, or processing pipelines) with clear technical reasoning.
  • Communicate technical work through clean, well-documented/commented code and structured written reports that meet graduate-level academic standards.

Course prerequisites

Prerequisites: A basic understanding of logic, and programming is recommended. The course covers Python fundamentals, computational methods, algorithm design and analysis, and object-oriented programming. Active participation in hands-on exercise sessions is essential for success.

Facts

Level
Master
Type
Mandatory course – also offered as elective
ECTS
7.5
Course code
CDSCO2402U
Teaching periods
Autumn – semester
Academic year
2026/2027
Teaching language
English
Teaching Format
On campus
Exams
  • Written exam on campus
    Individual exam, winter
Assessment
  • 7 point grading scale