Advanced Topics in Data Science (4569) |
| Language of instruction : English |
| Credits: 3,0 | | | | Period: quarter 3 (3sp)  | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
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Sequentiality
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No sequentiality
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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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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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Quarter 3 (3,00sp)
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| Written evaluation during teaching period | 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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Learning outcomes Master of Statistics and Data Science
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- EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student is capable of acquiring new knowledge. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
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2nd year Master Data Science
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J
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Exchange Programme Statistics
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J
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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