Identifying Data 2020/21
Subject (*) Data Analysis in Biology Code 610G02044
Study programme
Grao en Bioloxía
Descriptors Cycle Period Year Type Credits
Graduate 1st four-month period
 Fourth Optional 6
Language
Spanish
Teaching method Hybrid
Prerequisites
Department Matemáticas
Coordinador
Estevez Perez, Maria Graciela
 E-mail
graciela.estevez.perez@udc.es
Lecturers
,
Estevez Perez, Maria Graciela
Jacome Pumar, Maria Amalia
 E-mail
rebeca.pelaez@udc.es
graciela.estevez.perez@udc.es
maria.amalia.jacome@udc.es
Web
General description Esta materia proporciona un primeiro contacto con técnicas estatísticas avanzadas incluíndo: modelización estatística, ferramentas estatísticas para a análise de datos, procedementos de crítica e diagnose dos resultados e interpretación dos resultados en termos do problema proposto. Os obxectivos son:
- Adquirir unha visión ampla e integrada dos métodos estatísticos resaltando de cada un deles os seus obxectivos e condicións de aplicabilidade.

- Obter os coñecementos precisos para unha análise crítica e rigorosa dous resultados acadados.

- Complementar a aprendizaxe da metodoloxía co apoio de software informático
Contingency plan 1. Modificacións nos contidos
Non se realizarán cambios

2. Metodoloxías
*Metodoloxías docentes que se manteñen
Probas resposta breve: computan na avaliación (20%) e faranse online mediante a plataforma Moodle.udc.es
Traballo tutelado: Os traballos tutelados consistirán na aplicación a algún caso práctico, proporcionado polos docentes, dalgunhas das técnicas estatísticas estudadas. Computan na avaliación (40%).

*Metodoloxías docentes que se modifican
As sesións maxistrais: non computan na avaliación. Impartiranse usando TEAMS na franxa horaria que ten asignada a materia no calendario de aulas da facultade.
As prácticas TIC: non computan na avaliación. Na modalidade presencial consistían en análises de datos usando software estatístico (R) nas aulas de informática. En caso de non poder impartir presencialmente ás prácticas, realizaranse guións detallados das mesmas para que o alumnado poida realizalas pola súa conta e utilizaranse as sesións programadas no cronograma do 4º curso do Grao en Bioloxía para comentalas, corrixilas e tratar de resolver as dúbidas do alumnado facendo uso da aplicación Teams.
As probas obxectivas: computan na avaliación (40%). Pasan de se realizar presencialmente a realizarse de forma online usando moodle.udc.es

3. Mecanismos de atención personalizada ao alumnado

Ferramenta Temporalización
Correo Electrónico Diariamente. De uso para facer consultas, solicitar encontros virtuais para resolver dúbidas e facer o seguimento dos traballos tutelados.
Vídeo conferencia (Teams) Realizaranse titorías individuais e grupais a demanda dos alumnos, e fixadas previamente mediante correo electrónico.
Moodle Diariamente, segundo a necesidade do alumnado. Dispoñen de “foros temáticos” asociados aos módulos da materia, para formular as consultas necesarias

4. Modificacións na avaliación

Metodoloxía Peso na cualificación Descrición
Proba final 40% Para cada bloque, consistirá na resolución dunha serie de cuestións tipo test ou de resposta breve sobre a aplicación e interpretación dos métodos estudados na materia. Para aqueles estudantes que teñan dificultades técnicas na realización da proba final mediante Moodle, existe a posibilidade de realizar a proba noutra hora ou día.

Traballo en grupo 40% Traballo en grupo que consiste na aplicación a algún caso práctico, proporcionado polos docentes, dalgunhas das técnicas estatísticas estudadas facendo uso do software R
Cuestionarios test en Moodle 20% Realización de cuestionarios de tipo test en Moodle de cada un dos temas da materia.


*Observacións de avaliación:
A cualificación obtida nos traballos gardarase ó longo do presente curso académico. No suposto de non ter presentado o (os) traballo(s) tutelado(s) na primeira oportunidade de xaneiro, será requirido na segunda oportunidade (xullo). Os estudantes a tempo parcial e/ou con dispensa académica deberán tamén entregar este(s) traballo(s).
Para aprobar a materia é necesario ter aprobada por separado cada un dos bloques dos que consta a materia. En caso contrario, de ter superado só un bloque ou ningún, a cualificación final será como máximo un 4.5. Para superar cada bloque é preciso que a cualificación da proba final non sexa inferior a 3 puntos (sobre 10).
En calquera das dúas oportunidades anuais figurará un NON PRESENTADO unicamente naqueles casos nos que o alumnado non entregue os traballos nin se presente ó exame oficial.
Todas as observación previas son aplicables aos estudantes a tempo parcial e/ou con dispensa académica.

5. Modificacións da bibliografía ou webgrafía
Non se realizarán cambios. Xa dispoñen de tódolos materiais de traballo dixitalizados en Moodle.

Study programme competencies
Code Study programme competences
A21 Deseñar modelos de procesos biolóxicos.
A26 Deseñar experimentos, obter información e interpretar os resultados.
A30 Manexar adecuadamente instrumentación científica.
B2 Resolver problemas de forma efectiva.
B3 Aplicar un pensamento crítico, lóxico e creativo.
B4 Traballar de forma autónoma con iniciativa.
B5 Traballar en colaboración.
B6 Organizar e planificar o traballo.
B10 Exercer a crítica científica.

Learning aims
Learning outcomes Study programme competences
To learn how to design experiments, to acquire and develop skills to interpret and discuss statistical results. A21
A26
A30
 B2
B3
B5
B6
B10
Developing critical and creative thinking skills to address problems in an effective way. B2
B3
B4
B5
B6
B10

Contents
Topic Sub-topic
Lineal regression models

 Simple linear regression model
Multiple linear regression model
Other regression models
Design and analysis of experiments
 Basic principles. Planning experiments
ANOVA models with one and more than one sources of variation
Complete blocks designs
Designs including random effects
Introduction to covariance analysis
Introduction to multivariate analysis Description of multivariate data
Principal component analysis
Multivariate analysis of variance
Discriminant analysis
Cluster analysis

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Supervised projects A21 A26 A30 B2 B3 B4 B5 B6 B10 4 10 14
ICT practicals A26 A30 B2 B3 B10 14 22.4 36.4
Problem solving A26 B2 B3 B5 B10 6 9.6 15.6
Guest lecture / keynote speech A26 B2 B3 B6 B10 22 55 77
Short answer questions A21 B2 B3 B6 2 0 2
Objective test A21 A26 A30 B2 B3 B4 B6 B10 3 0 3
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Supervised projects Students should develop one or two practical works related to the subject contents. These works could be defended during a pre-established seminar.
ICT practicals Practical classes in the computer lab conducted to provide some knowledge on the use of statistical software (mainly the R-commander package). These classes are specifically designed to learn the elementary use of the package and to interpret its outputs. Use of software helps to focus attention on the statistical issues rather than on the calculation.
Problem solving Solving real problems in order to use statistical techniques fluently, empashizing their practical application.
Guest lecture / keynote speech Lectures where the basic theoretical principles of the subject are presented together with properly illustrated practical examples.
Short answer questions Multiple choice and short answer questions to assess the progress for each unit of the subject. They will be online using moodle.udc.es.
Objective test Final exam on the theoretical and practical contents of the subject. This exam consists in answering a list of short questions and/or solving some longer exercises in a reasoned way.

Personalized attention
Methodologies
Supervised projects
Description
There will be personalized advice sessions during the development of the practical works. These sessions will take place by means of the interaction teacher/students at the moment of solving the different activities suggested in class: solving doubts, correcting mistakes, suggesting proper approaches to deal with the proposed problems and reviewing initial versions of the works. In addition to this, students will have the opportunity of receiving personalized advice in the office of the teachers. Personalize advice may be also received via online (e-mail, virtual platform,...).

Part-time students are not required to defend their works in class, but these works must be provided to the teachers for their assessment. Part-time students can also receive personalized assistance using both face-to-face and virtual approaches.

Assessment
Methodologies Competencies Description Qualification
Supervised projects A21 A26 A30 B2 B3 B4 B5 B6 B10 Application of several statistical techniques to practical cases. 40
Objective test A21 A26 A30 B2 B3 B4 B6 B10 Test for skills assessment. 40
Short answer questions A21 B2 B3 B6 Multiple choice and short answer questions to assess the progress for each unit of the subject. They will be online using moodle.udc.es. 20
 
Assessment comments

The
objective tests, in each of the two opportunities, will consist of multiple
choice and short answer questions, related to the application of the studied statistical
methodologies and the interpretation of the corresponding results. The
supervised projects will be practical projects in group, with the
implementation of some of the different statistical methodologies to a real
data set given by the teacher/s, using statistical software (R). The score of
the supervised projects will be kept during the current academic course. In
case one (or both) supervised project(s) is (are) not submitted for the first
opportunity in January, it (they) will be required for the second opportunity
in July. Part-time students and/or with academic exemption must submit these
supervised projects as well.

To pass the subject, it will be
strictly necessary to pass each block separately. Otherwise, if only one or no
blocks are passed, the final score will be 4.5 at most. To pass each block, it
is necessary that the score of the objective test is not lower than 3 out of
10.

For any of the two opportunities to pass the subject,
the “NON PRESENTADO” grade will be given only to the students who did not
submit any of the supervised projects nor take the objective test.

All these remarks are applied to the
part-time students and/or with academic exemption.

Sources of information
Basic

· Kuehl, R.O. (2001) Diseño de Experimentos.Principios estadísticos para el diseño y análisis de investigaciones. 2nded. Thomson Learning.

· Milton, J.S. (2001). Estadística para Biología y Ciencias de la Salud , 3ª Edición,McGraw-Hill.

· Montgomery, D.C. (2005) Design and Analysis of Experiments. 6thEdtition J. Wiley and Sons.

· Peña, D. (2002). Análisis de DatosMultivariantes . McGraw-Hill.
Complementary

·  Box, G.E.P., Hunter, W.G. & Hunter, J.S. (1978). Statistics for Experimenters. An introduction to Design, Data Analysis, and Model Building. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, Inc.

 ·  Cao,R. et al. (2001). Introducción a la Estadística y sus aplicaciones. Ed. Pirámide, Madrid.

 ·  Dean, A. & Voss, D.  (1999) Design and Analysis of Experiments. Springer-Verlag, New York.

 ·  Gibbons, J.D. & Chakraborti, S. (1992). Nonparametric Statistical Inference. 3rd ed. Marcel Dekker, New York (1992).

 ·  Jobson, J.D. (1992). Applied Multivariate Analysis. Vol. II: Categorical and Multivariate Methods.  Springer Texts in Statistics, Springer-Verlag: New York.

 ·  Martín Andrés, A. & De Dios Luna del Castillo, J. (1994). Bioestadística para las Ciencias de la Salud. 4ª Edición. Eds. NORMA S.A.

 ·  Millard, S.P. & Neerchal, N.J. (2001) Environmental Statistics with S-Plus. Springer. CRC Press LLC.

 ·  Prat, A., Tort-Martorell, X., Groma, P. & Pozueta, L. (1997). M’etodos estadísticos. Control y mejora de la calidad. Edicions UPC (Universitat Politécnica de Catalunya).

 ·  Zar, J.H. (1996). Biostatiscal Analysis. 3rd. ed. Prentice Hall International.

Recommendations
Subjects that it is recommended to have taken before
Statistics/610G02005

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments

1- Attendance and participation in both theoretical and practical lectures.

2- Complete all the problems solved in the development of the classes, with and without using statistical software.

3- Complement the materials provided by the teachers using the recommended references.

4- Continually review the work done in class by solving questionnaires and proposed problems.

5- Active participation in seminars scheduled for the presentation and defense of practical works.

6- Regular use of statistical software.

7- Application of statistical techniques to address problems arising in other subjects.

8- Take advantage of a regular participation in the personalized tutorial sessions.


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.