ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Biology
Call: First Semester
Teaching: Sin docencia (Extinguida)
Enrolment: No Matriculable | 1st year (Yes)
Purchase the handle, under a practical approach, of the diverse technicians that allow a correct and rigorous approach, collected, analysis and interpretation of data especially focused to the field of the Biotechnology.
These aims centre in the foundations and basic models of the statistical methods and in the exploratory and inference analysis of the data.
- Know obtain, organise, present and describe groups of data by means of descriptive statistics.
- Know calculate the probability of an event.
- Know the characteristics and distribution of the random variables.
- Know describe and apply methods of statistical inference.
- Know describe and apply mathematical models of linear regression and analysis of the variance.
- Know the foundations of the linear programming.
Lectures (27 hours)
Chapter 1. Descriptive statistics (4 hours)
Chapter 2. Probability (4 hours)
Chapter 3. Random variables (4 hours)
SubChapterject 4. Introduction to the statistical inference: punctual estimate, intervals of confidence and contrasts of hypothesis. Inference in normal populations (7 hours)
Chapter 5. Linear regression (3 hours)
Chapter 6. Analysis of the variance (3 hours)
Chapter 7. Introduction to the linear programming (2 hours)
Seminars (11 hours)
- Resolution of the exercises proposed in each chapter.
Computer Labs (10 hours)
- Implementation of the methods with the statistical software R.
Basic Bibliography:
Cao, R., et al, 2001. Introducción a la estadística y sus aplicaciones. Madrid: Pirámide.
Milton, J.S., 2007. Estadística para biología y ciencias de la salud. 3ª ed. amp. Madrid: Mc Graw-Hill Interamericana.
Verzani, J., 2005. Using R for Introductory Statistics. Boca Raton, FL: Chapman and Hall/CRC. Disponible en línea: <https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf>
Complementary Bibliography:
Dalgaard, P., 2008. Introductory statistics with R. 2nd ed. New York: Springer.
Daniel, W.W., 2009. Biostatistics: basic concepts and methodology for the health sciences. 9th ed. Hoboken, NJ: John Wiley & Sons.
Peña, D., 2005. Fundamentos de estadística. Madrid: Alianza.
Rosner, B., 2011. Fundamentals of biostatistics. 7th ed. Boston: Brooks/Cole.
Rius Díaz, F. and Wärnberg, J., 2014. Bioestadística. 2ª ed. Madrid: Paraninfo.
In this subject the student will practise a series of basic competitions, general and transversal, own of the Degree in Biotechnology, and some specific competitions of this matter in particular. More specifically, in this subject work the following competences:
Basic competences and generals
CG1 - Know the concepts, methods and results more important of the distinct branches of the Biotechnology.
CG2 - Apply the theoretical knowledges-practical purchased in the approach of problems and the research of his solutions so much in academic contexts like professionals.
CG3 - Know obtain and interpret information and notable results and obtain conclusions in subjects related with the Biotechnology.
CG4 - Be able to transmit information so much by writing as of oral form and to debate ideas, problems and relative solutions to the Biotechnology, in front of a general or skilled public.
CG5 - Study and learn of autonomous form, with organisation of time and resources, new knowledges and technical in Biotechnology and purchase capacity to work in team.
CB1 - That the students have showed to possess and comprise knowledges in an area of study that splits of the base of the secondary education general, and is used to find to a level that, although it supports in books of text advanced, includes also some appearances that involve pertinent knowledges of the avant-garde of his field of study
CB2 - That the students know to apply his knowledges to his work or vocation of a professional form and possess the competitions that are used to to show by means of the preparation and defence of arguments and the resolution of problems inside his area of study
CB3 - That the students have the capacity to gather and interpret notable data (usually inside his area of study) to issue trials that include a reflection on notable subjects of social type, scientific or ethical.
CB4 - That the students can transmit information, ideas, problems and solutions to a so much specialised public as no skilled.
CB5 - That the students have developed those skills of necessary learning to undertake back studies with a high degree of autonomy.
Transversal competences
CT4 - Interpret experimental results and identify consistent and incosistent elements.
CT7 - Keep an ethical commitment.
CT6 - Reason critically
Specific competences
CE1 - Know do calculations, analyse data and interpret own experimental results of the fields of Biotechnology.
- The matter has six credits ECTS delivered in 27 hours of lectures, 11 hours of interactive teaching and 10 hours of computer. labs
- The classes will develop in the classroom assigned doing use fundamentally of the blackboard and of presentations. It will boost the participation of the students in the classes, especially in the most practical appearances. Also they will argue and they will resolve diverse exercises billed in bulletins, that will be available to students in the Campus Virtual, to boost his personal work, using also to evaluate his use.
- The students will have the support of the Campus Virtual of the USC, through the page of the course, to have of access to the programs, bibliography and distinct bulletins of exercises, as well as to notes of some subjects and information on voluntary complementary activities and tools of communication.
Continuous assessment (30%): the continuous assessment will carry out along the semester. It will consist of the following elements:
-Resolution of exercises of the bulletins associated to each chapter and exhibition in the seminars. By means of this activity evaluate the competences: CG1, CG2, CG3, CG4, CB2, CB3, CB4, CB5, CT4, CT7, CT6, CE1.
-Delivery of exercises resolved and properly drafted for his evaluation by the profesorado. By means of this activity evaluate the competences: CG1, CG2, CG3, CG4, CB2, CB3, CB4, CB5, CT4, CT7, CT6, CE1.
-Intermediate test that will include different theoretical-practical questions about the contents of the subject. By means of this activity evaluate the competences: CG3, CB5, CT4, CT6, CE1.
-Evaluation forms of the computer labs, that willperform in the own hours of interative teaching. By means of this activity evaluate the competences: CG3, CB5, CT4, CT7, CE1.
The qualification obtained by continuous assessment will be conserved only for the second call of the same course.
Final exam (70%): the final exam will consist of several theoretical and practical questions about the contents of the matter, inside which will be able to include the interpretation of results obtained with the statistical software used in the computer labs. By means of this activity evaluate the competences: CG1, CG2, CG3, CG5, CB1, CB2, CB5, CT7, CE1.
The repeater students will have the same assessment system. It considers that one student has presented to an announcement when he/she has participated in assessment activities evaluation that suppose more than 50% of the total assessment.
For the cases of fraudulent realisation of exercises or proofs will be of application the collected in the "Normativa de evaluación del rendimiento académico de los estudiantes y de revisión de calificaciones".
In this subject, the student must attend 48 hours of lectures (27 lectures and 21 interactive). For each lecture hour, about 1.5 hours is considered necessary for the revision of concepts and bibliography consulting.
For the interactive part, one hour of self-study for each session hour would be enough for revising the class work. Apart from this work, the students must be aware of the importance of being able to solve problems (from the exercise assigments or from the recommended bibliography).
- Assistance to all the educational activities and follow-up of the tasks proposed by the professors of the subject.
- Do use of the samll groups to consult any doubt that can arise.
- Devote to the study of the subject a regularly distributed time along the quarter.
The teaching material will be made available to students through the USC Virtual Campus.
Maria Angeles Fernandez Sotelo
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813210
- mangeles.fernandez.sotelo [at] usc.es
- Category
- Professor: University Lecturer
Mercedes Conde Amboage
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mercedes.amboage [at] usc.es
- Category
- Professor: Temporary PhD professor
Maria Vidal Garcia
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariavidal.garcia [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 13:00-14:00 | Grupo /CLE_01 | Spanish, Galician | Classroom 01. Charles Darwin |
| Tuesday | |||
| 13:00-14:00 | Grupo /CLE_01 | Galician, Spanish | Classroom 01. Charles Darwin |
| 01.23.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 04: James Watson and Francis Crick |
| 06.27.2024 16:00-20:00 | Grupo /CLE_01 | Classroom 03. Carl Linnaeus |