Guías Docentes Electrónicas
1. General information
Course:
ANÁLISIS ESTADÍSTICO DE DATOS
Code:
53341
Type:
ELECTIVE
ECTS credits:
4.5
Degree:
316 -
Academic year:
2019-20
Center:
5 -
Group(s):
10 
Year:
4
Duration:
First quarter
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: NOELIA GARCIA RUBIO - Group(s): 10 
Building/Office
Department
Phone number
Email
Office hours
Facultad de Ciencias Económicas y Empresariales. Despacho 3.13
ECO.POL/ HAC. PUB.,EST.ECO./EMP Y POL EC
2176
noelia.garcia@uclm.es
Ver la página web de la facultad y Moodle de la asignatura

2. Pre-Requisites

It is recommended to have coursed the subjects on Statistics for Economics and Statistical Inference

3. Justification in the curriculum, relation to other subjects and to the profession

Today it is very common, in the world of Economics and Business, to have a great amount of data and manage computer tools for proper extraction of the statistical information they contain.
In this process, the knowledge and use of appropriate statistical techniques is fundamental to the discovery of new and meaningful relationships and behavior patterns within the data. The aim of the course is to provide students with the tools necessary for the representation, description and extraction of patterns and relationships between variables in multidimensional data, which is known in the statistical literature as "data mining".


4. Degree competences achieved in this course
Course competences
Code Description
E03 Ability to find economic data and select relevant facts.
E06 Application of profesional criteria to the analysis of problems, based on the use of technical tools.
E11 Diagnosis and assessment skills to conduct structural and cyclical reports, as well as economic forecast summaries on the reality of the economy in Spain, the European Union and in any of the product sectors and factor markets. To do so, it will be necessary to understand and use common handbooks, as well as articles and, in general, leading edge bibliography in the core subjects of the curriculum.
G01 Possession of the skills needed for continuous, self-led, independent learning, which will allow students to develop the learning abilities needed to undertake further study with a high degree of independence.
G03 Develop oral and written communication skills in order to prepare reports, research projects and business projects and defend them before any commission or group of professionals (specialised or non-specialised) in more than one language, by collecting relevant evidence and interpreting it appropriately so as to reach conclusions.
G04 Ability for the use and development of information and communication technology in the development of professional activity.
G05 Capacity for teamwork, to lead, direct, plan and supervise multidisciplinary and multicultural teams in both national and international environments.
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Train the student to listen to and defend arguments orally or in writing
Train the student to work out problems in creative and innovative ways.
Additional outcomes
Description
The student will obtain the ability to conduct a preliminary analysis of the data, identifying relevant information and preparing it for further analysis. The student will know identify the appropriate statistical technique, based on the data available and taking into account their nature, to achieve the objectives. The student will get the ability to properly apply each statistical technique through appropriate tools, mainly using the statistical programming environment R. The student will be able to draw the relevant conclusions and know how to analyze and transmit them appropriately for decision making in a business economic scope.
6. Units / Contents
  • Unit 1: Introduction to Multivariate Analysis
    • Unit 1.1: Data and measurement scale
    • Unit 1.2: Introduction to Data Mining and software R for statistical computing
    • Unit 1.3: Descriptive and exploratory data analysis
    • Unit 1.4: Detection of outliers
    • Unit 1.5: Treatment of non-response
  • Unit 2: Clasification and comparison of groups
    • Unit 2.1: Linear discriminant analysis
    • Unit 2.2: Cluster Analysis
    • Unit 2.3: Analysis of variance
  • Unit 3: Data reduction methods
    • Unit 3.1: Principal component analysis
    • Unit 3.2: Factor analysis
  • Unit 4: Models for qualitative data analysis
    • Unit 4.1: Contingency tables and measures of association
    • Unit 4.2: Correspondence factor analysis
    • Unit 4.3: Multidimensional scaling
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences ECTS Hours As Com R Description
Class Attendance (theory) [ON-SITE] Lectures E03 E06 E11 G04 1 25 N N N
Class Attendance (practical) [ON-SITE] Combination of methods E03 E06 E11 G01 G03 G04 G05 0.5 12.5 Y N N
Study and Exam Preparation [OFF-SITE] Self-study E03 E06 E11 G01 G04 1.2 30 N N N
Writing of reports or projects [OFF-SITE] Group Work E03 E06 E11 G01 G04 G05 0.86 21.5 Y N Y
Other off-site activity [OFF-SITE] Self-study E11 G01 G03 G04 0.74 18.5 Y N Y
Other on-site activities [ON-SITE] Combination of methods E06 E11 G01 G03 G04 G05 0.1 2.5 N N N
Progress test [ON-SITE] Assessment tests E03 E06 E11 G01 G03 G04 0.02 0.5 Y N N
Final test [ON-SITE] Assessment tests E06 E11 G01 G03 G04 0.08 2 Y Y Y
Total: 4.5 112.5
Total credits of in-class work: 1.7 Total class time hours: 42.5
Total credits of out of class work: 2.8 Total hours of out of class work: 70
As: Assessable training activity
Com: Training activity of compulsory overcoming
R: Rescheduling training activity
8. Evaluation criteria and Grading System
  Grading System  
Evaluation System Face-to-Face Self-Study Student Description
Assessment of active participation 10.00% 0.00% The active attitude of the student will be assessed in the classroom.
Fieldwork assessment 30.00% 0.00% At the begining of the course working groups will be created and they will develop a project along the course. These projects will be supervised by the teacher and may need to be exposed at the end of the course.
Assessment of problem solving and/or case studies 10.00% 0.00% The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme.
Progress Tests 10.00% 0.00% The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme.
Final test 40.00% 0.00% The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme.
Total: 100.00% 0.00%  

Evaluation criteria for the final exam:
The final test may be replaced by increasing the weight of the part corresponding to the resolution of problems or cases
Specifications for the resit/retake exam:
The student can only recover the qualifications of group work and problem solving (handing it over again according to teacher recommendations) and final test (exam). Qualifications of the other sections will be retained but without possibility of recovery
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Other on-site activities [PRESENCIAL][Combination of methods] 2.5
Progress test [PRESENCIAL][Assessment tests] .5
Final test [PRESENCIAL][Assessment tests] 2

Unit 1 (de 4): Introduction to Multivariate Analysis
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 6.67
Class Attendance (practical) [PRESENCIAL][Combination of methods] 3.33
Study and Exam Preparation [AUTÓNOMA][Self-study] 7.5
Writing of reports or projects [AUTÓNOMA][Group Work] 5.75
Other off-site activity [AUTÓNOMA][Self-study] 4
Group 10:
Initial date: 16-09-2019 End date: 07-10-2019

Unit 2 (de 4): Clasification and comparison of groups
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 5.83
Class Attendance (practical) [PRESENCIAL][Combination of methods] 2.91
Study and Exam Preparation [AUTÓNOMA][Self-study] 7.5
Writing of reports or projects [AUTÓNOMA][Group Work] 5
Other off-site activity [AUTÓNOMA][Self-study] 4.5
Group 10:
Initial date: 07-10-2019 End date: 29-10-2019

Unit 3 (de 4): Data reduction methods
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 5.83
Class Attendance (practical) [PRESENCIAL][Combination of methods] 2.91
Study and Exam Preparation [AUTÓNOMA][Self-study] 7.5
Writing of reports or projects [AUTÓNOMA][Group Work] 5
Other off-site activity [AUTÓNOMA][Self-study] 5
Group 10:
Initial date: 04-11-2019 End date: 25-11-2019

Unit 4 (de 4): Models for qualitative data analysis
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 6.67
Class Attendance (practical) [PRESENCIAL][Combination of methods] 3.35
Study and Exam Preparation [AUTÓNOMA][Self-study] 7.5
Writing of reports or projects [AUTÓNOMA][Group Work] 5.75
Other off-site activity [AUTÓNOMA][Self-study] 5
Group 10:
Initial date: 26-11-2019 End date: 17-12-2019

Global activity
Activities hours
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
 
Giudici, P.; Figini, S. Applied data mining for business and industry Chichester (UK) Wiley 978-0-470-05887-9 2009 Ficha de la biblioteca
Arriaza, Fernández, López, Muñoz, ... Estadística Básica con R y R-Commander Universidad de Cádiz  
Escobar Espinar, Modesto Análisis gráfico/exploratorio La Muralla Hespérides 84-7635-387-1 1999 Ficha de la biblioteca
Everitt, B.; Hothorn, T.. A handbook of statistical analyses using R Boca Raton ; London ; New York Chapman and Hall/CRC 978-1-4200-7933-3 2010 Ficha de la biblioteca
Everitt, B.; Hothorn, T.. An introduction to applied multivariate analysis with R New York Springer 978-1-4419-9649-7 2011 Ficha de la biblioteca
Gil Flores, Javier analisis factorial La Muralla- Hespérides.  
Gil Flores, Javier Análisis discriminante La Muralla ; Salamanca Hespérides 84-7133-704-5 2001 Ficha de la biblioteca
Johnson, Dallas E. Métodos multivariados aplicados al análisis de datos International Thomson Editores 968-7529-90-3 2000 Ficha de la biblioteca
Lévy, J.P. y Varela, J. Análisis Multivariable para las Ciencias Sociales Pearson/Prentice 2003  
Martínez Arias, María Rosario El análisis multivariante en la investigación científica La Muralla Hespérides 84-7635-386-3 1999 Ficha de la biblioteca
Peña, Daniel Análisis de datos multivariantes McGraw-Hill, Interamericana de España 84-481-3610-1 2002 Ficha de la biblioteca
Tattar, P. N.; Rumaiah, S. y Manjunath, B. G. A Course in Statistics in R Wiley 978-1-119-15272-9 2016  
Uriel Jiménez, Ezequiel Análisis multivariante aplicado : aplicaciones al marketing, Thomson 84-9732-372-6 2005 Ficha de la biblioteca



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