Educational guide
IDENTIFYING DATA							2024_25
Subject	DATA SCIENCE II					Code	00717021
Study programme	0717 - GRADO INGENIERÍA DATOS INTELIGENCIA ARTIFICIAL
Descriptors	Credit.	Type	Year	Period
	6	Compulsory	Third	First
Language
Prerequisites
Department	MATEMATICAS
Coordinador	GÓMEZ PÉREZ , JAVIER				E-mail	jgomp@unileon.es aquic@unileon.es
Lecturers	GÓMEZ PÉREZ , JAVIER QUIROS CARRETERO , ALICIA
Web	http://
General description
Tribunales de Revisión	Tribunal titular Cargo Departamento Profesor Presidente MATEMATICAS GARCIA FERNANDEZ , ROSA MARTA Secretario MATEMATICAS SANTAMARIA SANCHEZ , RAFAEL Vocal MATEMATICAS TROBAJO DE LAS MATAS , MARIA TERESA Tribunal suplente Cargo Departamento Profesor Presidente MATEMATICAS SUSPERREGUI LESACA , JULIAN JOSE Secretario MATEMATICAS VEGA CASIELLES , SUSANA Vocal MATEMATICAS SAEZ SCHWEDT , ANDRES

Competencias

Code
A18963
A18964
A18966
A18989
B5801
B5802
B5806
B5807
B5808
C2	CMECES2 That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study.
C5	CMECES5 That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

Learning aims

	Competences
	A18963 A18964 A18966 A18989	B5801 B5802 B5806 B5807 B5808	C2 C5

Contents

Topic	Sub-topic

Planning

Methodologies  ::  Tests
	Class hours	Hours outside the classroom	Total hours
Problem solving, classroom exercises	24	24	48
	6	9	15
Lecture	24	24	48
Extended-answer tests	6	33	39
(*)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
Lecture

Personalized attention

Lecture Problem solving, classroom exercises Extended-answer tests

Description

Assessment

	Description	Qualification
Lecture		Supondrá hasta un 5% de la calificación final.
Problem solving, classroom exercises		Supondrá hasta un 20% de la calificación final
Extended-answer tests		Entre un 80% y un 90% de la calificación final.
Other comments and second call

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	Everitt, B.S., Landau, S., Leese, M., Stahl, D, Cluster Analysis, Wiley, 2011 Green, P. E., Mathematical Tools for Applied Multivariate Analysis, Academic Press, 1976 Johnson, D. E., Métodos multivariados aplicados al análisis de datos, International Thomson Editores-ITP, 2000 Abraira, V. y Pérez de Vargas, A., Métodos multivariantes en Bioestadística, Centro de estudios Ramón Areces, 1996 Peña, D., Regresión y diseños de experimentos, Alianza Editorial, 2002 McQuarrie, A.D.R.; Tsai, C-L, Regression and Time Series Model Selection, World Scientific, 1999 Pérez, C., Técnicas de Análisis Multivariante de Datos., Pearson-Prentice Hall, 2008
Complementary

Recommendations

Subjects that it is recommended to have taken before

ANÁLISIS MATEMÁTICO I / 00717001 ANÁLISIS MATEMÁTICO II / 00717006 ÁLGEBRA LINEAL I / 00717007 LINEAR ALGEBRA II / 00717011 PROBABILITY CALCULUS / 00717012 DATA SCIENCE I / 00717017