| 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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In this course we will touch upon several advanced topics in data science, including:
- dimensionality reduction: curse of dimensionality, reducing high-dimensional space for feature selection and visualisation
- topological data analysis: understanding the underlying "shape" of complex high-dimensional data
- distributed data analysis: spark, probabilistic data structures, LSH
- association rule mining: finding relations between features in large datasets, incl frequent itemset mining
- advanced mathematics for data science.
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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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| 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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