Study programme competencies |
Code
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Study programme competences
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A3 |
CE3 - Reconocer y analizar problemas físicos, químicos, matemáticos, biológicos en el ámbito de la Nanociencia y Nanotecnología, así como plantear respuestas o trabajos adecuados para su resolución, incluyendo el uso de fuentes bibliográficas. |
A7 |
CE7 - Interpretar los datos obtenidos mediante medidas experimentales y simulaciones, incluyendo el uso de herramientas informáticas, identificar su significado y relacionarlos con las teorías químicas, físicas o biológicas apropiadas. |
B2 |
CB2 - Que los estudiantes sepan aplicar sus conocimientos a su trabajo o vocación de una forma profesional y posean las competencias que suelen demostrarse por medio de la elaboración y defensa de argumentos y la resolución de problemas dentro de su área de estudio |
B4 |
CB4 - Que los estudiantes puedan transmitir información, ideas, problemas y soluciones a un público tanto especializado como no especializado |
B5 |
CB5 - Que los estudiantes hayan desarrollado aquellas habilidades de aprendizaje necesarias para emprender estudios posteriores con un alto grado de autonomía |
B6 |
CG1 - Aprender a aprender |
B7 |
CG2 - Resolver problemas de forma efectiva. |
B8 |
CG3 - Aplicar un pensamiento crítico, lógico y creativo. |
B9 |
CG4 - Trabajar de forma autónoma con iniciativa. |
B10 |
CG5 - Trabajar de forma colaborativa. |
B11 |
CG6 - Comportarse con ética y responsabilidad social como ciudadano/a y como profesional. |
B12 |
CG7 - Comunicarse de manera efectiva en un entorno de trabajo. |
C3 |
CT3 - Utilizar las herramientas básicas de las tecnologías de la información y las comunicaciones (TIC) necesarias para el ejercicio de su profesión y para el aprendizaje a lo largo de su vida |
C7 |
CT7 - Desarrollar la capacidad de trabajar en equipos interdisciplinares o transdisciplinares, para ofrecer propuestas que contribuyan a un desarrollo sostenible ambiental, económico, político y social. |
C8 |
CT8 - Valorar la importancia que tiene la investigación, la innovación y el desarrollo tecnológico en el avance socioeconómico y cultural de la sociedad |
C9 |
CT9 - Tener la capacidad de gestionar tiempos y recursos: desarrollar planes, priorizar actividades, identificar las críticas, establecer plazos y cumplirlos |
Learning aims |
Learning outcomes |
Study programme competences |
Identify the need for the use of numerical and statistical methods in solving models of real problems, especially originated in nanoscience and nanotechnology |
A3 A7
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B2 B4 B5 B7 B8 B9 B10
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C7
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Know and acquire fluency in the handling of numerical methods for the solution of different problems, as well as knowing the conditions to approximate the solution |
A3 A7
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B2 B4 B5 B6 B7 B8 B9 B10
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Have criteria to select the most efficient numerical methods in different problems, especially those related to nanoscience and nanotechnology |
A3 A7
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B2 B4 B5 B6 B7 B8 B9 B10 B11 B12
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C7 C8
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Acquire knowledge about probability and statistical methods of modeling, data analysis, diagnosis and interpretation of results |
A3
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B2 B4 B5 B6 B7 B8 B9 B10 B11 B12
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C3 C7 C8 C9
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Manage software tools that implement the studied methodology and know how to analyze the results |
A3 A7
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B2 B4 B5 B6 B7 B8 B9 B10 B11 B12
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C3 C7
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Contents |
Topic |
Sub-topic |
Unit 0: Introduction to numerical methods |
Introduction to numerical methods. Erros. |
Unit 1: Numerical resolution of linear systems and numerical approximation fo eigenvalues
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- Direct methods (LU, Cholesky)
- Iterative methods (Jacobi, Gauss-Seidel)
- Aproximation of eigenvalues:: QR
- Aplications |
Unit 2: Numerical resolution of non-linear equations
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- Non-linear equations (bisection, Newton and variant, functional iteration)
- Non-linear systems (functional iteration, Newton)
- Aplications |
Unit 3: Interpolation, numerical derivation and integration |
- Interpolation (Lagrange, Chebyshev, Splines)
- Numerical derivation
- Numerical integration (middle point, trapezoid, Simpson, gaussian quadrature)
- Aplications |
Unit 4. Basic concepts on probability theory
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- Probability formulas
- Conditional probability and independent events
- Bayes' theorem
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Unit 5. Random variables |
- Discrete and continuos variables
- Normal distribution and Central Limit Theorem
- Applications in Nanoscience and Nanotechnology |
Unit 6. Introduction to Statistical Inference
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- Estimators and sampling distributions
- Linear regression
- Statistical analysis software tools |
Planning |
Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A3 B2 B4 B5 B6 B7 B11 C8 |
28 |
56 |
84 |
Problem solving |
A7 B8 B12 |
8 |
16 |
24 |
ICT practicals |
A3 A7 B2 B4 B10 C3 C7 C9 |
12 |
25 |
37 |
Mixed objective/subjective test |
B7 B9 C9 |
3 |
0 |
3 |
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Personalized attention |
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2 |
0 |
2 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Exhibition of the contents specified in the program of the subject, for which audiovisual media or blackboard will be used. |
Problem solving |
Sessions where relevant problems in the field of Science and Engineering will be presented, which will be solved both analytically and numerically: the student must be able to reach the solution of any problem using pencil and paper or alternatively using computer tools, and compare the results. |
ICT practicals |
Interactive practices in which relevant problems in the field of Science and Engineering will be solved.
In the part corresponding to Numerical Methods Units 0 - 3) we will programate in Python, and in the Statistical Methods part (Units 4-6) we will work with R using Rcmdr. |
Mixed objective/subjective test |
Development of issues and problems of the subject. |
Personalized attention |
Methodologies
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ICT practicals |
Problem solving |
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Description |
- Due to the diversity of the students and their training, a personalized orientation is recommended, which could be carried out through tutorials.
- Practices with ICT tools in problem solving, or teachers will help students in the development of two stated problems, as well as applications to problems in the field of Science and Engineering.
- The specific personalized attention measures for "Students with recognition of part-time dedication and academic waiver of attendance exemption" for the study of the subject, the continuous evaluation of the practices through ITC and the resolution of problems carried out attending, as far as possible, to your particular circumstances.
- In the part of Numerical Methods: With the aim of preparing students for the different continuous assessment tests, as well as for the final test; Group defenses will be made, of the problems raised. Its implementation will be determined jointly between the teacher and the students. They will take place in the teachers' office. The defenses will be distributed in groups, in four sessions of 10 minutes (for each group).
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Assessment |
Methodologies
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Competencies |
Description
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Qualification
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ICT practicals |
A3 A7 B2 B4 B10 C3 C7 C9 |
Resolución de problemas de carácter práctico empregando o lenguaxe de programación Python ou R. |
30 |
Problem solving |
A7 B8 B12 |
Resolución de problemas de carácter práctico.
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20 |
Mixed objective/subjective test |
B7 B9 C9 |
Proba que inclúe a resolución de cuestións e problemas da materia |
50 |
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Assessment comments |
The subject is organized in two parts: Numerical Methods (MNum) and Statistical Methods (MEst). The contents corresponding to part MNum are indicated in Units 0-3, and the contents corresponding to part MEst are indicated in Units 4-6. Each part will be qualified on 10 points: - The quatification of MNum (CNum) will be between 0 and 10 points.
- The qualification of MEst (CEst) will be between 0 and 10 points.
The final qualification of the subject will be the mean of the notes achieved in each of the two parts: Nota Final= (CNum + CEst)/2 The qualification for each one of the two parts of the subject is the following: - The MNum qualification is divided into three parts:
- Qualificaton of ITC practices (CP_1): between 0 and 3.5 points
- Qualification of problems resolution (CR_1): between 0 and 1.5 points
- Qualification of the mixed objetive test (CE_1): between 0 and 5 points.
The final qualification of MNum (CNUm) will be the sum of the three parts CP_1 + CR_1 + CE_1, if the qualification of the mixed objetive test is greater than 1.5 (over 5 points). In another case, the final qualification will be the qualification obtained on the objective test, CE_1. The continuous evaluation qualification of MNum, CP_1 + CR_1, will be carried out through two small mixed tests where the student will have to solve problems analytically and numerically (via Python).
The final qualification of the part will be: CNum= CP_1 + CR_1 + CE_1 - The qualification of the part MEst is divided into three parts:
- Qualification of ITC practices (CP_2): between 0 and 2.5 points
- Qualification of problems resolution (CR_2): between 0 and 2.5 points
- Qualification of the mixed objetive test (CE_2): between 0 and 5 points.
The final qualification of MEst (CEst) will be the sum of the three parts CP_2 + CR_2 + CE_2, if the qualification of the mixed objetive test is greater than 1.5 (over 5 points). In another case, the final qualification will be the qualification obtained on the objective test, CE_2. The continuous evaluation qualification of MNum, CP_1 + CR_1, will be carried out through two small tests. The final qualification of MEst will be: CEst= CP_2 + CR_2 + CE_2
The final qualifcation of the subject will be the mean of CNum and CEst: NotaFinal = (CEst + CNum)/2
In the second opportunity of the evaluation: - In the second opportunity, the student will only have to attend the part of the exam which he/she failed in the first opportunity:
- In the part of MNum, the grades related to the practices through ICT (CR_1) and problem solving (CP_1) will be kept.
- In the part of MEst, the grades related to the practices through (CR_2) and problem solving (CP_2) will be kept.
A Non-Attended state will be assigned to those students who do not attend the final mixed test. -Observations on the “Students with recognition of part-time dedication and academic exemption from attendance exemption”: The specific personalized attention measures for the “Students with recognition of part-time dedication and academic exemption from attendance exemption” for the study of subject, the continuous evaluation of the practices through TIC and of the resolution of problems carried out attending, as far as possible, to your particular circumstances.
- Observations on fraud: "During the performance of the practical test, on either occasion, except as otherwise indicated, the use of any device with Internet access is prohibited. If during the performance of the practical test, there are indications In case of unauthorized use of these devices, the student will be expelled from the classroom, and will proceed according to Law 3/2022, of February 24, on university coexistence and the disciplinary regulations of the UDC student body. Fraudulent completion of tests and/or activities will directly imply a failing grade ("0") in the subject in the corresponding call, invalidating any grade obtained in all activities for the next opportunity, if any, within the same academic course. It will be considered fraudulent to carry out activities, proposed to be completed in person in the classroom, that are done from outside the classroom, proceeding according to Law 3/2022, of February 24, on university coexistence and the disciplinary regulations of the UDC student "
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Sources of information |
Basic
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James F. Epperson (2021). An Introduction to Numerical Methods and Analysis (3rd Ed.). Wiley
F. Rius Díaz, F.J. Barón López (2005). Bioestadística. Thomson.
A.J. Arriaza Gómeza (2008). Estadística básica con R y R-Commander. Servicio Publicaciones UCA.
R. Cao Abad y otros (2001). Introducción a la estadística y sus aplicaciones. Ed. Pirámide
J. Douglas Faires, R. Burden (2014). Métodos Numéricos (7ª ed). Thomson
Steven C. Chapra, Raymond P. Canale (2019). Métodos Numéricos para ingenieros (7º ed). McGrawHill |
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Complementary
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Jeffrey J. Heys (2017). Chemical and Biomedical Engineering Calculations Using Python. Wiley
J. Baró LLinas, (1998). Estadística Descriptiva, Cálculo de probabilidades e Inferencia estadística (tres volúmenes). Ed. Parramón
W. Navidi (2006). Estadística para ingenieros y científicos (1ª Ed) . Mc Graw-Hill
Jaan Kiusalaas (2013). Numerical Methods in Engineering with Python 3. Cambridge University Press
Alicia Cordero Barbero, José Luís Hueso Pagoaga, Eulalia Martínez Molada, Juan Ramón Torregrosa Sanc (). Problemas resueltos de métodos numéricos. Paso a paso. Paraninfo |
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Recommendations |
Subjects that it is recommended to have taken before |
Fundamentals of Mathematics/610G04001 | Advanced Calculus /610G04009 | Fundamentals of Computing Science/610G04010 |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
Differential Equations/610G04016 |
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Other comments |
Daily study of the contents treated in the classroom, complementing them with the recommended bibliography. To help achieve an immediate sustainable environment and comply with point 6 of the "Environmental Declaration of the Faculty of Science (2020)", the documentary work carried out in this area: Most will be requested in virtual format and computer support.
- Gender
perspective: as stated in the transversal competences of the title
(C4), the development of a critical, open and respectful citizenship
with diversity in our society will me promoted, highlighting the equal
rights of students without discrimination based on gender or sexual
condition. An inclusive language will be used in the material and during
the development of the lessons.
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