Competencies / Study results |
Code
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Study programme competences / results
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A1 |
CE1 - Capacidade para utilizar con destreza conceptos e métodos propios da matemática discreta, a álxebra lineal, o cálculo diferencial e integral, e a estatística e probabilidade, na resolución dos problemas propios da ciencia e enxeñaría de datos. |
A2 |
CE2 - Capacidade para resolver problemas matemáticos, planificando a súa resolución en función das ferramentas dispoñibles e das restricións de tempo e recursos. |
A3 |
CE3 - Capacidade para a análise de datos e a comprensión, modelado e resolución de problemas en contextos de aleatoriedade. |
B1 |
CB1 - Que os estudantes demostrasen posuír e comprender coñecementos nunha área de estudo que parte da base da educación secundaria xeral, e adóitase atopar a un nivel que, aínda que se apoia en libros de texto avanzados, inclúe tamén algúns aspectos que implican coñecementos procedentes da vangarda do seu campo de estudo |
B5 |
CB5 - Que os estudantes desenvolvesen aquelas habilidades de aprendizaxe necesarias para emprender estudos posteriores cun alto grao de autonomía |
B6 |
CG1 - Ser capaz de buscar e seleccionar a información útil necesaria para resolver problemas complexos, manexando con soltura as fontes bibliográficas do campo. |
C1 |
CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
C2 |
CT2 - Estimular a capacidade para traballar en equipos interdisciplinares ou transdisciplinares, para ofrecer propostas que contribúan a un desenvolvemento sustentable ambiental, económico, político e social. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Have knowlegde about statistical techniques and knowing how to use them for the exploratory data analysis. |
A1 A2 A3
|
B1 B5 B6
|
C1
|
Have knowlegde and understand the general concepts about probability models. |
A1 A2 A3
|
B1 B5 B6
|
C1 C2
|
Knowing how to model in simple random contexts using probabilistic tools. |
A1 A2 A3
|
B1 B5 B6
|
C1
|
Knowing how to use auxiliary computer tools for Statistics: statistical packages and programming languages with statistical orientation; and knowing how to critically interpret the results. |
A1 A2 A3
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B1 B5 B6
|
C1
|
Contents |
Topic |
Sub-topic |
Probability |
Definition of probability. Properties
Conditional probability. Bayes’ theorem
|
Univariate random variables |
Discrete random variables
Continuous random variables
Central limit theorem
Applications: Reliability and simulation
|
Multivariate random variables |
Bivariate discrete random variables
Bivariate continuous random variables
Marginal distributions
Conditionated distributions
Independent random variables
Characteristic measures
Multivariate random variables |
Descriptive statistics |
Frequency distributions
Graphical representations
Location and dispersion measures
Two dimensional statistical variable
Linear simple regression |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A1 A3 B5 |
30 |
48 |
78 |
Laboratory practice |
C1 C2 |
20 |
16 |
36 |
Seminar |
A2 B6 |
10 |
10 |
20 |
Mixed objective/subjective test |
B1 |
4 |
0 |
4 |
|
Personalized attention |
|
12 |
0 |
12 |
|
(*)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 |
Students will receive lectures where the professor, with the help of relevant audiovisual media, will present the theoretical and practical contents of the subject. Participation and debate will be encouraged at all times. |
Laboratory practice |
Laboratory practices will be held in a computer lab. It will be learned how to use the free statistical software R, and its programming structures. Statistical studies using both real and simulated data will be performed. |
Seminar |
Seminars will reinforce both the applied nature of the subject and its interactivity. Students will be able to express their doubts and concerns regarding the subject, and they will have the opportunity to perform, with the professor supervision, similar questions to those proposed in the exams. Additionally, with a very individualized attention, they will be able to complete the lab practices. |
Mixed objective/subjective test |
Students will have to show proficiency in the theoretical aspects of the subject and their ability to solve problems in the field of probability and statistics. |
Personalized attention |
Methodologies
|
Seminar |
Guest lecture / keynote speech |
Laboratory practice |
|
Description |
For problem solving, it will be important to personally help students with the questions that may arise. This attention will also serve, on the one hand, to the professor to detect potential problems in the methodology used to teach the subject and, on the other hand, to the students to strengthen theoretical knowledge and to express their concerns about the subject. |
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Assessment |
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
Seminar |
A2 B6 |
During the course, students will prove their interest in the subject and his mastery of it by performing one written test. |
10 |
Mixed objective/subjective test |
B1 |
The final exam will consist of a mixed theoretical/practical test. |
60 |
Laboratory practice |
C1 C2 |
In order to evaluate the degree of understanding and learning of these practices, 2 or 3 evaluation tests will be scheduled, which will be carried out during the laboratory classes. |
30 |
|
Assessment comments |
On the date set by the faculty in its annual program, students will take the final test of the subject, in which they will have to answer theoretical questions, solve theoretical-practical issues, and calculate the solution of various problems; for this test the students will only be allowed to bring with them the material expressly authorized. In the second opportunity, the grades obtained by continuous evaluation (the control and the laboratory practice tests) are maintained and the student only has to repeat the final test. All aspects related to "academic dispensation", "dedication to study", "permanence" and "academic fraud" will be governed in accordance with the current academic regulations of the UDC.
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Sources of information |
Basic
|
Eguzkitza Arrizabalaga, J.M. (2014). Laboratorio de estadística y probabilidad con R. Gami Editorial
Cao, R., Francisco, M., Naya, S., Presedo, M.A., Vázquez, M., Vilar, J.A. y Vilar, J.M. (2001). Introducción a la Estadística y sus aplicaciones. Ediciones Pirámide |
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Complementary
|
Gonick, L. y Smith, W. (2001). Á estatística ¡en caricaturas!. SGAPEIO
Blasco Lorenzo, A. y Pérez Díaz, S. (2015). Modelos aleatorios en ingeniería. Paraninfo
Montgomery, D.C. y Runger, G.C. (2004). Probabilidad y Estadística aplicadas a la Ingeniería. McGraw-Hill
Devore, J.L. (2008). Probabilidad y Estadística para Ingeniería y Ciencias. Thomson
Walpole, R.E., Myers, S.L. y Myers, R. (2000). Probabilidad y Estadística para Ingenieríos. Prentice Hall
Hernández, V., Ramos, E. y Yáñez, I. (2007). Probabilidad y sus aplicaciones en Ingeniería Informática. Ediciones Académicas
Ugarte, M.D., Militino, A.F., Arnholt, A.T. (2008). Probability and Statistics with R. Chapman and Hall/CRC
Horgan, J.M. (2009). Probability with R. An Introduction with Computer Science Applications. Wiley |
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Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
Regression Models/614G02012 | Statistical Modeling of High Dimensional Data/614G02013 | Statistical Inference/614G02007 |
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