| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Advising sequentiality bound on the level of programme components
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Following programme components are advised to also be included in your study programme up till now.
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Business statistics (1738)
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6.0 stptn |
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Advanced Mathematics 1 (1536)
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6.0 stptn |
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Advanced mathematics 2 (4034)
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3.0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st Master of Business and Information Systems Engineering | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| The holder of the degree models, designs and evaluates solutions for business and IT problems to support decision-making at different levels in a complex context. (Problem-solving capacity) | - EC
| The holder of the degree uses data science and IT to design decision support systems that provide useful insights with which the quality of decisions can be improved. (Programme-specific competencies) |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student has adequate understanding (written and oral) of the English language.
The student masters the program language Python.
The student can apply basic statistical tests and master mathematical concepts like equations, formulas.
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The following data mining/machine learning methods will be covered
- classification and estimation
- network analysis
- clustering
- association analysis
Applications
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Lecture ✔
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Small group session ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The score of the paper (25%) is transferred. There is only a retake of the written closed-book exam (75%) |
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| Compulsory textbooks (bookshop) |
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| Recommended reading |
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Data Mining: Practical Machine Learning Tools and Techniques,Ian H. Witten; Eibe Frank; Mark A. Hall,3,Morgan Kaufmann,9780128042915,Beschikbaar als e-book: https://www-sciencedirect-com.bib-proxy.uhasselt.be/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques |
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| Exchange Programme Business Economics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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The student has adequate understanding (written and oral) of the English language.
The student masters the program language Python.
The student can apply basic statistical tests and master mathematical concepts like equations, formulas.
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The following data mining/machine learning methods will be covered
- classification and estimation
- network analysis
- clustering
- association analysis
Applications
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Lecture ✔
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Small group session ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The score of the paper (25%) is transferred. There is only a retake of the written closed-book exam (75%) |
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| Compulsory textbooks (bookshop) |
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| Recommended reading |
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Data Mining: Practical Machine Learning Tools and Techniques,Ian H. Witten; Eibe Frank; Mark A. Hall,3,Morgan Kaufmann,9780128042915,Beschikbaar als e-book: https://www-sciencedirect-com.bib-proxy.uhasselt.be/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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