|
Type A
|
Code |
Competences Specific | | | A1.1 |
Effectively apply knowledge of basic, scientific and technological subjects pertaining to engineering. |
| | A3.1 |
Ability to solve mathematical problems that can arise in engineering. Ability to use knowledge on: linear algebra; geometry; differential geometry; differential and integral calculus; differential equations and partial derivatives; numerical methods; numerical algorithms; statistics and optimisation. |
|
Type B
|
Code |
Competences Transversal | | | B1.5 |
Use ICT to efficiently manage information and knowledge (CT2) |
| | B4.1 |
Learn effective ways to assimilate knowledge and behaviour. |
|
Type C
|
Code |
Competences Nuclear |
|
Type A
|
Code |
Learning outcomes |
| | A1.1 |
Aplica correctament els principis matemàtics que es puguin plantejar en enginyeria, àlgebra lineal, geometria, geometria diferencial, càlcul diferencial i integral, equacions diferencials i en derivades parcials, mètodes numèrics, algorítmica numèrica, estadística i optimització.
| | | A3.1 |
Adquireix les tècniques més elementals del càlcul numèric i les aplica amb l'ajuda d'un llenguatge de programació estructurat d'alt nivell en models.
Coneix els mecanismes estadísticament correctes per a una anàlisi eficient de dades: interpretació i presa de decisions sobre els valors de paràmetres físics o químics.
Coneix els mètodes més usuals d'optimització i els sap utilitzar en la resolució de problemes de l'àmbit de l'enginyeria.
|
|
Type B
|
Code |
Learning outcomes |
| | B1.5 |
Coneix el maquinari bàsic dels ordinadors.
Coneix el sistema operatiu com a gestor del maquinari i el programari com a eina de treball.
Utilitza programari per a comunicació: editors de textos, fulls de càlcul i presentacions digitals.
Utilitza programari per a comunicació virtual: eines interactives (web, moodle, blocs..), correu electrònic, fòrums, xat, vídeo-conferències, eines de treball col·laboratiu etc.
Localitza i accedeix a la informació de manera eficaç i eficient.
| | | B4.1 |
Desenvolupa estrategies pròpies per resoldre problemes i trobar solucions.
|
|
Type C
|
Code |
Learning outcomes |
| Topic |
Sub-topic |
| 1. Descriptive statistics. Mean, variance and standard deviation. |
|
| 2. Probability distribution models: binomial, Poisson, normal. |
|
| 3. Point estimation and confidence intervals estimation. |
|
| 4. Hipothesis testing. |
|
| 5. Analysis of variance. |
|
| 6. Least-squares aproximation. Linear regression and multiple linear regression. |
|
| 7. Introduction to optimization methods. Search of maxima and minima. Lagrange multipliers. |
|
| 8. Introduction to ordinary differential equations (ODE). Analytical solutions for first and second degree linear ODEs. |
|
| 9. Introduction to partial derivatives differential equations. Separable variables. |
|
| 10. Introduction to differential geometry. |
|
| Methodologies :: Tests |
| |
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
| Introductory activities |
|
1 |
1.5 |
2.5 |
| Lecture |
|
22 |
33 |
55 |
| Problem solving, exercises in the classroom |
|
14 |
21 |
35 |
| IT-based practicals in computer rooms |
|
14 |
21 |
35 |
| Personal attention |
|
1 |
1.5 |
2.5 |
| |
| Short-answer objective tests |
|
2 |
8 |
10 |
| Short-answer objective tests |
|
2 |
8 |
10 |
| |
(*) 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 |
Introduction of the subject where the lecturer discusses the content to be worked on, the objectives to be evaluated, the methodology to be used, and the evaluation system. |
| Lecture |
The lecturer explains the theoretical content of each topic. |
| Problem solving, exercises in the classroom |
The lecturer solves problems in classroom. |
| IT-based practicals in computer rooms |
Students are asked to do and deliver practicals, carried out with a computer, and related to the contents that are being worked on. These practicals are part of the continuous assessment of the subject. |
| Personal attention |
Students can receive personal attention in person or telematically during the hours of attention to students, and during practical hours in classroom. |
|
Description |
|
Time that each lecturer has reserved to attend to and solve students' doubts. |
|
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
| IT-based practicals in computer rooms |
|
Students will have to solve, with a computer, problems about various contents of the subject. The practical exercises will be assessed. |
0-20% |
| Short-answer objective tests |
|
Individual test of a synthesis character on the contents developed during the first part of the subject. |
40-50% |
| Short-answer objective tests |
|
Individual test of a synthesis character on the contents developed during the second part of the subject. |
40-50% |
| Others |
|
|
|
| |
|
Other comments and second exam session |
|
Continuous assessment: The practice grade will only be taken into account when it is higher than
the average grade of the two partial tests. In this case, the weights of the
practice grade and the two partial tests will be 20%, 40% and 40%,
respectively. Otherwise, these weights will be 0%, 50% and 50%, respectively. Second call:
The final grade will consist
of 100% for the grade of an individual objective test on the content of the
entire subject. |
| Basic |
Mateo, J.M., Estadística pràctica pas a pas, Universitat Rovira i Virgili,
Zill, D.G.; Wright, W.S., Matemáticas avanzadas para ingeniería, McGraw-Hill,
|
|
| Complementary |
|
|
(*)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. |
|