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| Detailed information |
| Original study plan |
Master's programme Economic and Business Analytics 2025W |
| Learning Outcomes |
Competences |
| Problem-solving: Students can abstract practical problems as mathematical models and solve them. They know how to write down their problems in a formally concise way. They know how to approach the problems by mathematical and algorithmic means.
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| Skills |
Knowledge |
- Learning Outcome 4 (LO4): Analyze, apply and design basic sorting and graph algorithms
- Learning Outcome 5 (LO5): Find optima of differentiable functions
- Learning Outcome 6 (LO6): Solve systems of linear equations
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- Learning Outcome 1 (LO1): Understand the basic concepts of algorithm design
- Learning Outcome 2 (LO2): Understand foundations of logic, analytics, and linear algebra
- Learning Outcome 3 (LO3): Understand how to apply mathematical concepts to formalize and solve problems from the fields of economics and business analytics
Course Content
- Sorting algorithms
- Big O-notation and complexity
- Graphs and basic graph algorithms
- Shortest-path and spanning tree algorithms
- Logic
- Sequences
- Limits of functions
- Continuity
- Differentiability
- Optima of functions
- Basics of linear algebra
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| Criteria for evaluation |
Regular homework exercises that must be submitted online via Moodle. The homework exercises are worth 30 points. Exam at the end of the semester with 70 points. At least 35 points on the exam are needed to pass the course. There is a possibility to repeat it in case of negative results or scheduling issues (retry exam). The exam consists of programming tasks. It lasts 90 minutes.
Final grades will be given as follows:
| Points | Grade |
|---|
| 87.5 - 100.0 | 1 |
| 75.0 - 87.0 | 2 |
| 62.5 - 74.5 | 3 |
| 50 - 62.0 | 4 |
| 0.0 - 49.5 | 5 |
Both the homework exercises and the exam cover all the learning outcomes
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| Methods |
The course uses a combination of different teaching methods in order to
- maximize the motivation and attention of the students.
- address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred information inputs, supported by slides and literature
- Development of content in collaboration with the students on the computer and the black board
- Development of solutions to exercise problems in groups, followed by joint discussions with the whole group
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| Language |
English |
| Study material |
- Slides
- In-class programming exercises with solutions
- Reading material
- Cormen, Leiserson, Rivest, Stein, Introduction to Algorithms, current edition, MIT Press
- Celia, Nice, Elliott Advanced Mathematics
- Pointers to additional literature
(All content is provided via Moodle)
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| Changing subject? |
No |
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