Educational guide

IDENTIFYING DATA

2023_24

Subject

DATA ANALYSIS

Code

01745001

Study programme

1745 - Máster Universitario en Investigación en Biotecnología y Biomedicina

Descriptors

Credit.

Type

Year

Period

Compulsory

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 results interpretation in fundamental Biology 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	SUSPERREGUI LESACA , JULIAN JOSE
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
A18955	B5753 B5754 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5764 B5765 B5766 B5767 B5768	C1 C2 C3 C4 C5
A18955	B5753 B5754 B5755 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5763 B5764 B5765 B5766 B5767 B5768	C1 C2 C3 C4 C5
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





I. Introduction 1. Introduction to data análisis 2. Introduction to R and RStudio II. Probability 1. Probability review, random variables and known distributions 2. Convergence theorems and their relationship with statistical inference III. Considerations about study design 1. Considerations about study design IV. Descriptive statistics 1. Descriptive statistics and exploratory data analysis V. Statistical inference 1. Basics of inferential statistics and study of different perspectives 2. Most commonly used tests in data analysis in biotechnology and biomedicine 3. Regression

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
Personal tuition

Lecture

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
		20% - 40%
Problem solving, classroom exercises		30% - 50%

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	Ruxton, G. & Colegrave, N., Experimental design for the life sciences, Oxford University Press, Xie, Y.; Allaire, J.J. & Grolemund, G., R Markdown: The Definitive Guide, Chapman & Hall/CRC, https://bookdown.org/yihui/rmarkdown/ Agresti, A., Klingenberg, B., Franklin, C. & Posner, M., Statistics: The Art and Science of Learning From Data, Global Edition,

Complementary	Everitt, E. & 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 Lavine, M., Introduction to Statistical Thought, , https://people.math.umass.edu/~lavine/Book/book.p Wickham, H. & Grolemund, G., R for Data Science, O'Reilly, https://r4ds.had.co.nz/index.html

Recommendations