1. General information
Course:
BIOSTATISTICS AND BIOINFORMATICS
Code:
310935
Type:
ELECTIVE
ECTS credits:
6
Degree:
2351 - MASTER DEGREE PROGRAMME IN PHYSICS AND MATHEMATICS-FISYMAT
Academic year:
2020-21
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
Lecturer:
VICTOR MANUEL CASERO ALONSO
- Group(s):
20
Building/Office
Department
Phone number
Email
Office hours
Politécnico/2-A15
MATEMÁTICAS
926052867
victormanuel.casero@uclm.es
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
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 | |
| Detect the existing relationship between variables and calculate the necessary parameters to adjust linear and non-linear models between these variables | |
| 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 | |
| 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 (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
| Class Attendance (theory) [ON-SITE] | Lectures | 1.04 | 26 | Y | N | |||
| Class Attendance (practical) [ON-SITE] | Practical or hands-on activities | 0.48 | 12 | Y | N | |||
| Workshops or seminars [ON-SITE] | Lectures | 0.16 | 4 | Y | N | |||
| Writing of reports or projects [OFF-SITE] | Guided or supervised work | 0.4 | 10 | Y | N | |||
| Individual tutoring sessions [ON-SITE] | Other Methodologies | 0.24 | 6 | N | N | |||
| Study and Exam Preparation [OFF-SITE] | Self-study | 3.68 | 92 | 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 (It will be essential to overcome both continuous and non-continuous assessment).
8. Evaluation criteria and Grading System
| Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
| Assessment of active participation | 10.00% | 0.00% | Assessment of active participation |
| Assessment of activities done in the computer labs | 15.00% | 20.00% | Labs related with the topics |
| Theoretical papers assessment | 20.00% | 25.00% | Reports about topics |
| Final test | 55.00% | 55.00% | Final exam |
| Total: | 100.00% | 100.00% |
According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).
Evaluation criteria for the final exam:
-
Continuous assessment:Correct approach of the problems.
Correct results.
Correct written expression. -
Non-continuous evaluation:Same as continuous assessment.
Specifications for the resit/retake exam:
Same as the final exam.
Specifications for the second resit / retake exam:
Same as the final exam.
9. Assignments, course calendar and important dates
| Not related to the syllabus/contents | |
|---|---|
| Hours | hours |
10. Bibliography and Sources
