Language of instruction : English |
Exam contract: not possible |
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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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
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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Prerequisites |
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Elementary command of mathematical principles: differential equations, integration, differentiation, matrix computation |
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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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|
|
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
|
|
|
|
|
|
|
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 | |
|
|
 
|
Prerequisites |
|
Elementary command of mathematical principles: differential equations, integration, differentiation, matrix computation |
|
 
|
Recommended course material |
|
Course material will be provided via blackboard |
|
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|
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1 examination regulations art.1.3, section 4. |
2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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Legend |
SBU : course load | SP : ECTS | N : Dutch | E : English |
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