Guías Docentes Electrónicas
1. General information
Course:
BIOESTADÍSTICA Y BIOINFORMÁTICA
Code:
310935
Type:
ELECTIVE
ECTS credits:
6
Degree:
2351 -
Academic year:
2019-20
Center:
Group(s):
20 
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: MARIANO AMO SALAS - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Facultad de Medicina / 1.35
MATEMÁTICAS
926295300 ext.6843
Mariano.Amo@uclm.es
6 horas a la semana. Se especificarán al comienzo de las clases.

Lecturer: VICTOR MANUEL CASERO ALONSO - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Politécnico/2-A15
MATEMÁTICAS
926295300, ext. 6402
victormanuel.casero@uclm.es
Disponible en Campus Virtual.

Lecturer: LICESIO JESUS RODRIGUEZ ARAGON - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Edificio Sabatini / 1.47
MATEMÁTICAS
6489
l.rodriguezaragon@uclm.es
Disponible en Campus Virtual y en https://intranet.eii-to.uclm.es/tutorias Pedir cita previa por correo electrónico.

2. Pre-Requisites

It is advisable to have realized a subject of Basic Statistics.

3. Justification in the curriculum, relation to other subjects and to the profession

In the current research context, with the usual use of data, it is necessary to include in the curriculum a subject that provides the student with a wide range of statistical tools for the analysis of data.


4. Degree competences achieved in this course
Course competences
Code Description
CB06 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB07 Apply the achieved knowledge and ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to the area of study
CB08 Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of knowledge and judgments
CB09 Know how to communicate the conclusions and their supported knowledge and ultimate reasons to specialized and non-specialized audiences in a clear and unambiguous way
CB10 Have the learning skills which allow to continue studying in a self-directed or autonomous way
CE05 Know how to obtain and interpret physical and/or mathematical data that can be applied in other branches of knowledge
CE08 Ability to model, interpret and predict from experimental observations and numerical data
CG01 Know how to work in a multidisciplinary team and manage work time
CG02 Ability to generate and independently develop innovative and competitive proposals in research and professional activity in the scientific field of Physics and Mathematics
CG03 Present publicly the research results or technical reports, to communicate the conclusions to a specialized court, interested persons or organizations, and discuss with their members any aspect related to them
CG04 Know how to communicate with the academic and scientific community as a whole, with the company and with society in general about Physics and/or Mathematics and its academic, productive or social implications
CG05 Gain the ability to develop a scientific research work independently and in its entirety. Be able to search and assimilate scientific literature, formulate hypotheses, raise and develop problems and draw conclusions from the obtained results
CT01 Promote the innovative, creative and enterprising spirit
CT03 Develop critical reasoning and the ability to criticize and self-criticize
CT05 Autonomous learning and responsibility (analysis, synthesis, initiative and teamwork)
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Obtain and use epidemiological data and assess trends and risks for health decision making
Be able to perform different studies and survival analysis
Use statistic techniques to give confidence intervals for a population parameter and the confidence level of this interval
Summarize large datasets, using statistical measures and graphical representations
Apply statistic contrasts to validate hypotheses on a data set for one, two or more populations
Apply statistic inference techniques from a sample to formulate valid conclusions for the population, also measuring the confidence level of the conclusions obtained
Apply statistic techniques through the use of software, especially R
Know the correct use and interpretation of biostatistics to critically evaluate scientific and health information
Know the statistic aspects of bioinformatics
Build the various demographic health indicators
Detect the existing relationship between variables and calculate the necessary parameters to adjust linear and non-linear models between these variables
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Probabilistic Models
  • Unit 2: Stochastic processes
  • Unit 3: Statistical Inference
  • Unit 4: Demography
  • Unit 5: Designs of epidimiological research
  • Unit 6: Survival analysis
  • Unit 7: Linear and non-linear models
  • Unit 8: ANOVA and regression models
  • Unit 9: Statistical methods in Bioinformatics
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences ECTS Hours As Com R Description
Class Attendance (theory) [ON-SITE] Lectures 1.04 26 Y N N
Class Attendance (practical) [ON-SITE] Practical or hands-on activities 0.48 12 Y N N
Workshops or seminars [ON-SITE] Lectures 0.16 4 Y N N
Writing of reports or projects [OFF-SITE] Guided or supervised work 0.4 10 Y N N
Individual tutoring sessions [ON-SITE] Other Methodologies 0.24 6 N N N
Study and Exam Preparation [OFF-SITE] Self-study 3.68 92 N N N
Total: 6 150
Total credits of in-class work: 1.92 Total class time hours: 48
Total credits of out of class work: 4.08 Total hours of out of class work: 102
As: Assessable training activity
Com: Training activity of compulsory overcoming
R: Rescheduling training activity
8. Evaluation criteria and Grading System
  Grading System  
Evaluation System Face-to-Face Self-Study Student Description
Assessment of active participation 10.00% 0.00% Assessment of active participation
Assessment of activities done in the computer labs 15.00% 0.00% Labs related with the topics
Theoretical papers assessment 20.00% 0.00% Reports about topics
Final test 55.00% 0.00% Final exam
Total: 100.00% 0.00%  

Evaluation criteria for the final exam:
Evaluation criteria not defined
Specifications for the resit/retake exam:
Evaluation criteria not defined
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours

10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
Bioestadística amigable / Elsevier, 978-84-9022-500-4 2014 Ficha de la biblioteca
Box, George E. P. Estadística para investigadores : diseño, innovación y descu Reverté, 978-84-291-5044-5 2008 Ficha de la biblioteca
Irala Estévez, Jokin de Epidemiología aplicada / Ariel, 978-84-344-3725-8 2011 Ficha de la biblioteca
Montgomery, Douglas C. Diseño y análisis de experimentos / Limusa Wiley, 978-968-18-6156-8 2014 Ficha de la biblioteca
Peña, Daniel Análisis de datos multivariantes / McGraw-Hill, Interamericana de España, 978-84-481-3610-9 2010 Ficha de la biblioteca
Peña, Daniel Análisis de series temporales Alianza 978-84-206-6945-8 2010 Ficha de la biblioteca
Peña, Daniel Fundamentos de estadística / Alianza Editorial, 978-84-206-8380-5 2008 Ficha de la biblioteca
Peña, Daniel Regresión y diseño de experimentos Alianza Editorial 978-84-206-9389-7 2010 Ficha de la biblioteca



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