Educational guide
IDENTIFYING DATA							2023_24
Subject	ADVANCED STATISTICS II					Code	00511021
Study programme	0511 - G.MARKETING E INVESTIGACIÓN DE MERCADOS
Descriptors	Credit.	Type	Year	Period
	6	Compulsory	Third	First
Language	Castellano
Prerequisites
Department	ECONOMIA Y ESTADISTICA
Coordinador	ABAD GONZÁLEZ , JULIO IGNACIO				E-mail	jiabag@unileon.es ralve@unileon.es
Lecturers	ABAD GONZÁLEZ , JULIO IGNACIO ÁLVAREZ ESTEBAN , RAMÓN
Web	http://
General description	Multivariate statistical analysis and machine learning techniques for categorical data. Applications of specific statistical software ((R/ RStudio and Flourish).
Tribunales de Revisión	Tribunal titular Cargo Departamento Profesor Presidente ECONOMIA Y ESTADISTICA HUERGA CASTRO , MARIA DEL CARMEN Secretario ECONOMIA Y ESTADISTICA VALLEJO PASCUAL , MARIA EVA Vocal ECONOMIA Y ESTADISTICA BLANCO ALONSO , PILAR Tribunal suplente Cargo Departamento Profesor Presidente ECONOMIA Y ESTADISTICA ARIAS SAMPEDRO , CARLOS Secretario ECONOMIA Y ESTADISTICA GARCIA GALLEGO , ANA BELEN Vocal ECONOMIA Y ESTADISTICA RODRIGUEZ FERNANDEZ , MARIA DEL PILAR

Competencias

Code
A16272
A16274
A16278
B5100
B5101
B5102
B5107
B5108
B5109
C3	CMECES3 That students have the ability to gather and interpret relevant data (normally within their area of study) to make judgments that include reflection on relevant issues of a social, scientific or ethical nature.

Learning aims

	Competences
	A16272 A16274 A16278	B5100 B5101 B5102 B5107 B5108	C3
	A16272 A16274 A16278	B5100 B5101 B5102 B5107 B5108	C3
	A16272 A16278	B5100 B5101 B5107 B5108 B5109	C3
	A16272 A16278	B5100 B5101 B5107 B5108 B5109	C3
	A16272 A16278	B5100 B5101 B5107 B5108 B5109	C3

Contents

Topic	Sub-topic
	Unit 1. Introduction Unit 2. Data visualization Unit 3. Text mining Unit 4. Correspondence analysis and multidimensional scaling Unit 5. Log-linear models and association rules analysis Unit 6. Hierarchical segmentation methods: classification trees Unit 7. Discrete response models: logistic regression Unit 8. Conjoint analysis

Planning

Methodologies  ::  Tests
	Class hours	Hours outside the classroom	Total hours
	40	40	80
	10	30	40
	0	20	20
Lecture	0	0	0
	7.5	0	7.5
Mixed tests	2.5	0	2.5
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies   ::  

	Description
Lecture

Personalized attention

Description

Assessment

	Description	Qualification
		30%
		20%
Mixed tests		50%
Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	Lévy Mangin, J.-P. (dir.) y Varela Mallou, J. (dir.), Análisis multivariable para las ciencias sociales, Pearson Educación, 2003 Hair, J. F. et al., Análisis multivariante (5ª ed.), Pearson Prentice Hall, 1999 Aldás Manzano, J. y Uriel Jiménez, E., Análisis multivariante aplicado con R, Paraninfo, 2017 Jaggia, S., Kelly, A., Lertwachara, K. y Chen, L., Business analytics: communicating with numbers, McGraw-Hill, 2021 Yoon Hyup Hwang, Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R, Packt Publishing, 2019 Ruíz-Maya, L. y Martín-Pliego, F. J., Metodología estadística para el análisis de datos cualitativos, Centro de Investigaciones Sociológicas, 1990 Luque Martínez, T. (coord., coaut.), Técnicas de análisis de datos en investigación de mercados, Pirámide, 2000 Luque Martínez, T. (coord., coaut.), Técnicas de análisis de datos en investigación de mercados: entre lo real y lo ideal (2ª ed. ed. versión digital), Pirámide, 2012
Complementary	Correa Piñero, A. D., Análisis logarítmico lineal, La Muralla, 2002 Mateos-Aparicio, G.; Morales Hernández Estrada, A., Análisis multivariante de datos. Cómo buscar patrones de comportamiento en BIG DATA, Pirámide, 2021 Pere, R.; Pascual,V., Analítica visual. Como explorar, analizar y comunicar datos, Anaya Multimedia, 2021 Paczkowski, W. R. , Business Analytics: Data Science for Business Problems, Springer, 2022 Healy, K. J., Data visualization: a practical introduction, Princeton University Press, 2019 Evergreen, S. D. H., Effective data visualization: the right chart for the right data, SAGE, 2017 Pochiraju, B. y Seshadri, S. (eds.), Essentials of Business Analytics An Introduction to the Methodology and its Applications, Springer, 2019 Greenacre, M. J., La práctica del análisis de correspondencias, Fundación BBVA, 2008 Kassambara, A. , Machine Learning Essentials. Practical Guide in R, STDHA, 2017 Bécue, M., Minería de textos: aplicación a preguntas abiertas en encuestas, La Muralla, 2010 Ohri, A., R for Business Analytics, Springer, 2013 Lebart,L.; Morineau, A.; Piron, M., Statistique exploratoire multidimensionnelle (2ª ed.), Dunod, 1997 Knaflic, C. N., Storytelling con datos. Visualización de datos para profesionales de los negocioss, Anaya Editorial, 2017 Bécue, M., Textual data science with R, Taylor and Francis, 2018

Recommendations

Subjects that are recommended to be taken simultaneously

MARKET RESEARCH APPLICATIONS / 00511022

Subjects that it is recommended to have taken before

STATISTICS I / 00511003 STATISTICS II / 00511014 ADVANCED STATISTICS I / 00511018