It is recommended to have coursed the subjects on Statistics for Economics and Statistical Inference
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".
Course competences | |
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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. |
Course learning outcomes | |
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Description | |
Train the student to work out problems in creative and innovative ways. | |
Train the student to listen to and defend arguments orally or in writing | |
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. |
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | E03 E06 E11 G04 | 1 | 25 | N | N | ||
Class Attendance (practical) [ON-SITE] | Combination of methods | E03 E06 E11 G01 G03 G04 G05 | 0.5 | 12.5 | N | N | ||
Study and Exam Preparation [OFF-SITE] | Self-study | E03 E06 E11 G01 G04 | 1.2 | 30 | N | N | ||
Writing of reports or projects [OFF-SITE] | Group Work | E03 E06 E11 G01 G04 G05 | 0.86 | 21.5 | Y | N | ||
Other off-site activity [OFF-SITE] | Self-study | E11 G01 G03 G04 | 0.76 | 19 | Y | N | ||
Other on-site activities [ON-SITE] | Combination of methods | E06 E11 G01 G03 G04 G05 | 0.1 | 2.5 | N | N | ||
Final test [ON-SITE] | Assessment tests | E06 E11 G01 G03 G04 | 0.08 | 2 | Y | Y | ||
Total: | 4.5 | 112.5 | ||||||
Total credits of in-class work: 1.68 | Total class time hours: 42 | |||||||
Total credits of out of class work: 2.82 | Total hours of out of class work: 70.5 |
As: Assessable training activity Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Assessment of active participation | 5.00% | 0.00% | The active attitude of the student will be assessed in the classroom. |
Fieldwork assessment | 20.00% | 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 | 15.00% | 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 | 60.00% | 100.00% | 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% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
Other on-site activities [PRESENCIAL][Combination of methods] | 2.5 |
Final test [PRESENCIAL][Assessment tests] | 2 |
Unit 1 (de 4): Introduction to Multivariate Analysis | |
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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 | |
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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: 07-10-2019 | End date: 29-10-2019 |
Unit 3 (de 4): Data reduction methods | |
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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 | |
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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 | |
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Activities | hours |