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Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Numerical Methods for Data Science |
| Planning |
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| Identifying Data | 2023/24 | |||||||||||||
| Subject | Numerical Methods for Data Science | Code | 614G02033 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 1st four-month period |
Fourth | Optional | 6 | ||||||||||
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| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| ICT practicals | A2 B2 B3 B4 B9 B10 C1 C4 | 14 | 35 | 49 |
| Supervised projects | A2 B2 B3 B4 B7 B8 B9 B10 C1 C4 | 1.5 | 6 | 7.5 |
| Problem solving | A2 B2 B4 B9 B10 | 7 | 14 | 21 |
| Objective test | A2 B2 B3 B4 B7 B8 C1 | 2 | 4 | 6 |
| Collaborative learning | A2 B2 B3 B4 B7 B9 B10 C1 | 0.5 | 5 | 5.5 |
| Guest lecture / keynote speech | A2 B2 B3 B4 B8 B9 | 20 | 40 | 60 |
| Personalized attention | 1 | 0 | 1 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
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