Educational guide

IDENTIFYING DATA

2024_25

Subject

ADVANCED DATA ANALYSIS TOPICS

Code

01745014

Study programme

1745 - Máster Universitario en Investigación en Biotecnología y Biomedicina

Descriptors

Credit.

Type

Year

Period

Optional

First

Second

Language

Castellano

Ingles

Prerequisites

Department

MATEMATICAS

Coordinador

QUIROS CARRETERO , ALICIA

E-mail

aquic@unileon.es
jgomp@unileon.es

Lecturers

GÓMEZ PÉREZ , JAVIER

QUIROS CARRETERO , ALICIA

Web

http://

General description

Data analysis and interpretation of results in Biotechnology and Biomedicine

Tribunales de Revisión

Tribunal titular
Cargo	Departamento	Profesor
Presidente	MATEMATICAS	GARCIA FERNANDEZ , ROSA MARTA
Secretario	MATEMATICAS	ARIAS MOSQUERA , DANIEL
Vocal	MATEMATICAS	TROBAJO DE LAS MATAS , MARIA TERESA

Tribunal suplente
Cargo	Departamento	Profesor
Presidente	MATEMATICAS	RODRIGUEZ SANCHEZ , CRISTINA
Secretario	MATEMATICAS	CASTRO GARCIA , NOEMI DE
Vocal		SUAREZ CORONA , ADRIANA

Competencies

Type A	Code	Competences Specific
	A18955
Type B	Code	Competences Transversal
	B5753
	B5754
	B5755
	B5756
	B5757
	B5758
	B5759
	B5760
	B5761
	B5762
	B5763
	B5764
	B5765
	B5766
	B5767
	B5768
Type C	Code	Competences Nuclear
	C1
	C2
	C3
	C4
	C5

Learning aims

	Competences
Deepen advanced mathematical methods to analyse data obtained from biological or medical studies.	A18955	B5753 B5754 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5764 B5765 B5766 B5767 B5768	C1 C2 C3 C4 C5
To update knowledge of computer programmes useful for the mathematical analysis of data.	A18955	B5753 B5754 B5755 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5763 B5764 B5765 B5766 B5767 B5768	C1 C2 C3 C4 C5
To update knowledge related to the editing and presentation of scientific documents with mathematical content.	A18955	B5753 B5754 B5755 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5763 B5764 B5765 B5766 B5767 B5768	C1 C2 C3 C4 C5

Contents

Topic	Sub-topic
Topic 1: Experimental Design	Sub-topic 1: Experimental Design and Types of Studies in Biotechnology and Biomedicine
Topic II: Multivariate analysis	Sub-topic 2: Multivariate analysis
Topic III: Advanced methods for the analysis of biological and biomedical data	Sub-topic 3: Advanced methods for the analysis of biological and biomedical data

Planning

Methodologies :: Tests
	Class hours	Hours outside the classroom	Total hours
Problem solving, classroom exercises	20	20	40

Personal tuition	4	4	8
	0	15	15

Lecture	6	6	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
Problem solving, classroom exercises	On-site practical sessions: Experimental trials, problems or computer simulations. Students will carry out or follow an experimental protocol following good laboratory practices, as well as the guide of good practices for handling experimental animals and/or genetically manipulated microorganisms when necessary. In these sessions, the students and the teacher will discuss the results obtained, analysing their importance and interest.
Personal tuition	Specialised face-to-face online tutorials through educational support platforms.
	Self-teaching activities: - Analysis of relevant scientific articles on current topics related to the subject. - Preparation of written reports, oral presentations or seminars on experimental or bibiographical work. - Carrying out autonomous tasks online or in a computer classroom.
Lecture	Classroom theory sessions: Presentation or discussion of the contents of the subject.

Personalized attention

Personal tuition

Description

It will take place either in room 331 of the School of Industrial, Computer and Aerospace Engineering, or by videoconference. The interested student will have to make an appointment with the professor, preferably by e-mail.

Assessment

	Description	Qualification
Problem solving, classroom exercises	Attendance at laboratory practicals (attendance and active participation in all training activities).	30% - 50%
	Practicals report submission.	20% - 40%
Others	Project writing, presentation and discussion.	20% - 40%

Other comments and second call
In case a student does not pass the course in the ordinary call, the second call could be evaluated by means of a final exam covering all the contents of the course. Before resorting to the final exam, however, there will be the possibility of recovering some of the parts in the continuous evaluation.

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	Hardle, W.K. &amp; Simar, L., Applied multivariate statistical analysis, Springer, 2015 Montgomery, D.C., Design and Analysis of Experiments, Wiley, 2013 Quinn, G. &amp; Keough, M.J., Experimental design and data analysis for biologists, Cambridge University Press, 2002

Complementary	Everitt, E. &amp;amp; Hothorn, T., A Handbook of Statistical Analyses Using R, CRC Press, 2009 Milton, J.S., Estadística para Biología y Ciencias de la Salud, McGraw-Hill, 2014 Ruxton, G.D., Experimental design for the life sciences, Oxford University Press, 2016 Wickham, H. &amp;amp; Grolemund, G., R for Data Science, O'Reilly, https://r4ds.had.co.nz/index.html
	http://equator-network.org/

Recommendations

Subjects that are recommended to be taken simultaneously

DATA ANALYSIS / 01745001


Other comments
It is recommended to have taken a course in statistics during undergraduate studies.