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Teaching GuideTerm Faculty of Computer Science |
| Mestrado Universitario en Técnicas Estadísticas (Plan 2019) |
 Subjects |
| Nonparametric Methods |
| Learning aims |
| Identifying Data | 2019/20 | |||||||||||||
| Subject | Nonparametric Methods | Code | 614493111 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
First | Obligatory | 5 | ||||||||||
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| Learning outcomes | Study programme competences | ||
| To become familiar with basic techniques of nonparametric estimation of the probability distribution function, the probability density function and the regression function. | AC18 AC19 AC20 AC21 AC23 |
BJ1 BJ3 BJ5 BJ20 BJ21 |
CJ13 |
| Get the know-how to apply the main nonparamteric tests for goodness-of-fit and association. | AC18 AC19 AC20 AC21 AC23 |
BJ1 BJ3 BJ5 BJ20 BJ21 |
CJ13 |
| Get thorough knowledge about strengths and weaknesses of the nonparametric approach in data analysis. | AC16 AC17 AC19 AC21 AC23 |
BJ2 BJ17 BJ20 BJ21 |
CJ11 CJ13 |
| Develop autonomus competence to apply nonparametric tools in data analysis, in complex and/or multidisciplinary scenarios. | AC17 |
BJ18 |
CJ14 CJ15 |
| To know how present data analysis based on nonparametric techniques to both specialized and non-specialized audience. | BJ4 BJ19 |
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