| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Data Science | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student has basic knowledge in programming.
The student has basic knowledge of linear algebra such as eigenvalue problems and singular value decomposition
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Five different topics by five different professors: Distributed Data Analysis, Frequent Itemset Handling, Gaussian Processes and Bayesian Optimization, Dimensionality Reduction, Numerical Linear Algebra.
Each of these topics will have a task exam and a lab work, which will have an equal weight. In order to pass the course, it is necessary that a minimum of 5/20 is obtained for each topic. A short oral assessment will be part of the evaluation.
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Assignment ✔
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Lecture ✔
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Response lecture ✔
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Self-study assignment ✔
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Exercises ✔
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Homework ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 75 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Different texts from the various modules. |
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| Recommended course material |
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Course material will be provided via blackboard |
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| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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The student has basic knowledge in programming.
The student has basic knowledge of linear algebra such as eigenvalue problems and singular value decomposition
|
|
|
|
Five different topics by five different professors: Distributed Data Analysis, Frequent Itemset Handling, Gaussian Processes and Bayesian Optimization, Dimensionality Reduction, Numerical Linear Algebra.
Each of these topics will have a task exam and a lab work, which will have an equal weight. In order to pass the course, it is necessary that a minimum of 5/20 is obtained for each topic. A short oral assessment will be part of the evaluation.
|
|
|
|
|
|
|
|
|
Assignment ✔
|
|
|
|
Lecture ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
|
|
|
|
Exercises ✔
|
|
|
|
Homework ✔
|
|
|
|
Period 2 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 75 % |
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
Different texts from the various modules. |
|
|
| Recommended course material |
| |
Course material will be provided via blackboard |
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|
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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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