| 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 | |
 | second year Data Science - distance learning | 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. A minimum score of 5/20 is needed 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
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| Written evaluaton during teaching periode | 75 % |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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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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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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