 |
| Detailed information |
| Original study plan |
Master's programme Economic and Business Analytics 2025W |
| Learning Outcomes |
Competences |
| Students are able to implement an existing algorithm to solve a business (optimization) problem with performance considerations in mind, and compare different algorithmic approaches when there are several options.
Course Goals:
The goal of the course is to teach students how to approach and implement a complex algorithm for a given business task. The purpose is not on the design of new methods themselves, but rather on the practical aspects of implementing existing algorithms. A particular focus is set on optimization problems. Students are sensibilized to performance considerations when programming, and are given methodological insights to compare different algorithmic approaches. The course uses the programming language Julia.
|
|
| Skills |
Knowledge |
- Learning Outcome 1 (LO1): Use the programming language Julia. (k3)
- Learning Outcome 2 (LO2): Apply good programming practices, for better performances and readability of the code. (k3)
- Learning Outcome 3 (LO3): Implement algorithms to tackle business (optimization) problems. (k3)
- Learning Outcome 4 (LO4): Compare different algorithmic approaches for the same problem. (k4)
|
- Basics in Julia (variables, vectors, loops, conditionals, functions, programming using an IDE…)
- Implementing simple algorithms for business problems in Julia
- Algorithms to solve optimization problems using a solver (build and solve a mathematical model, query and do computations from the solution from a model, modify and resolve a model)
- Graphs (different representations, common operations on graph, dynamic programming, building a graph from a given problem, models based on graphs…)
- Setup a numerical experiment (reading files, running the same algorithm on different instances, analysis of results…)
|
|
| Criteria for evaluation |
In total, students have the possibility to reach 100 points, 40 (40 %) for the individual homework and 60 (60 %) for the final project. A minimum of 20 points at the homework and 30 points at the project is necessary in order to obtain a positive grade.
Final grades will be given as follows:
| Percent | Grade |
|---|
| 87,5 - 100 | 1 |
| 75 - 87 | 2 |
| 62,5 - 74,5 | 3 |
| 50 - 62,0 | 4 |
| 0 - 49,5 | 5 |
- Final project: Group project (1 to 3 students). Students have 5 weeks to complete it. A complex programming task as well as a report is asked. Submissions are done via moodle.
- Homework: there are 3 to 4 individual homework which have to be submitted via moodle. Feedback, if necessary, is done in class during exercise time.
Synchronization of learning outcomes and assessments:
- LO1: Project + Homework
- LO2: Project + Homework
- LO3: Project + Homework
- LO4: Project
|
| Methods |
The course uses a combination of different teaching methods in order to
- allow students to progress at their own pace
- address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred programming demonstrations
- In-class exercise time, where students progress at their own pace and receive personal help
- Individual homework exercises
- Group project
|
| Language |
English |
| Study material |
- Slides and notebooks for tutorials
- Exercise sheets with detailed solutions
- Homework exercises
(All content is provided via moodle)
|
| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
977ANA1PBTU19: IK Programming for Business Tasks (2019W-2022S)
|
|
