| Competences |
| Type A | Code | Competences Specific |
| A11 | Interpretar dades estadístiques en sistemes multivariables (reconeixement de patrons, tècniques de quimiometria) (competència de l'especialitat Microsistemes Electrònics). | |
| Type B | Code | Competences Transversal |
| B2 | Effective solutions to complex problems | |
| B3 | Critical, logical and creative thinking, and an ability to innovate | |
| Type C | Code | Competences Nuclear |
| C1 | Have an intermediate mastery of a foreign language, preferably English | |
| C2 | Be advanced users of the information and communication technologies | |
| C3 | Be able to manage information and knowledge |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A11 |
Realitza estratègies de disseny d'experiments òptimes quant a recursos utilitzats o exactitud dels resultats. Utilitza programes matemàtics (p.e. MATLAB) per analitzar les dades obtingudes i modelar els processos subjacents del fenomen sota estudi. Entén en profunditat la significació estadística dels resultats obtinguts i generalitza les conclusions a altres situacions semblants. Adquireix coneixements bàsics en ciències biomèdiques per dissenyar experiments i interpretar resultats en aquests àmbits. Utilitza les eines bàsiques de Quimiometria com el PCA o el PLS. Utilitza els models de xarxes neuronals més habituals (mapes de Kohonen, Xarxes backpropagation, Fuzzy Artmap...). Aplica algoritmes quimiomètrics a conjunts de dades biològiques (metabolòmica, proteòmica). | |
| Type B | Code | Learning outcomes |
| B2 |
Adopt realistic strategies for solving problems. | |
| B3 |
Identify the results of innovation. | |
| Type C | Code | Learning outcomes |
| C1 |
Understand the general meaning of texts that have non-routine information in a familiar subject area. | |
| C2 |
Use software for off-line communication: word processors, spreadsheets and digital presentations. | |
| C3 |
Locate and access information effectively and efficiently. |
| Contents |
| Topic | Sub-topic |
| Basic Statistics | Bàsic definitions Random variables Probability density function Cumulative probability function Normal distribution Common random distributions |
| Statistic result analysis | T-test Anova Manova Mann-Whitney Wilcoxon signed rank Krustall-wallis Friedman |
| Clàssical chemometry | Correlacion Regression PCA PCR PLS PLS-DA LDA Dendograms Heat Maps |
| Advanced Patern recognition | Kohonen Maps K-nearest neightbourt Fuzzy Art FeedForward neural networks and variants Support Vector Machines Fuzzy Artmap |
| Metabolomics introduction | Definition and bàsic concepts Measurement instruments Practical case scenarios and their data treatment |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 0 | 1 | ||||||
| Lecture |
|
20 | 20 | 40 | ||||||
| Personal tuition |
|
2 | 0 | 2 | ||||||
| Practical tests |
|
12 | 20 | 32 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | An introduction to the subject and content, explanation on the projects to be developed during the course and the grading system. |
| Lecture | It will basically consist on an oral exposition with slides, previosly handed to the students. Lectures should be interactive, with information exchange between students and the teacher, with questions. Arguments can be debated in class. |
| Personal tuition | Students will have, officially, 6 hours a week to ask about the subjects explained or to do their practical projects. Anyway, if possible, personal tuition will be given outside of the official hours as well if possible. |
| Personalized attention |
| Description |
| Students will be able to receive tuition a minimum of six hours every week. Anyway, the teacher will try to be flexible and allow tuition many more hours than those defined officially. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| Introductory activities |
|
Introductory activities will not be graded | 0 | ||||
| Lecture |
|
There will be 2 tests during the course | 60 | ||||
| Practical tests |
|
Each practical project will be graded accordingly | 40 | ||||
| Others |
| Other comments and second exam session | |||
| Sources of information |
| Basic |
Ron Wehrens, Chemometrics with R, 1st, 2011
Babak Shahbaba, Biostatistics with R, 1st, 2012 |
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| Complementary | |||||||||
| Recommendations |
| Subjects that continue the syllabus | ||
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| Subjects that are recommended to be taken simultaneously | ||
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| Subjects that it is recommended to have taken before | ||
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(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.