To pass the subject, the student is required to have certain conceptual and argumentative skills, and the equivalent of an introductory course in Calculus and Algebra.
The statistics course is the only course where the student will learn statistical techniques in the career. In statistics, the student must learn to make decisions based on data and how to represent them.
This course aims: - to describe and represent large volumes of data through the main measures of position and dispersion and be able to use graphical representation. - that the students acquire the necessary techniques for the modeling of situations that represent "Variability". - to base the decision-making process in general situations, on the basis of incomplete information. - to familiarize future computer scientists with the fundamental statistical techniques that directly reflect situations related to computer systems, and that they will use in the exercise of their profession.
In addition, you will learn to use powerful statistical programs, which can be obtained free of charge and allows you to download specific packages for a multitude of tasks.
Relationship with other subjects.
This is a subject of vital importance for the student to acquire a working method and a way of thinking and dealing with difficulties in a logical and rigorous manner. The subject will have an interdisciplinary sense relating the problems and examples proposed with other subjects and subjects of the study plan. The concepts studied will be used in almost all the subjects of the intensification of intelligent systems as well as in subjects related to the study of large amounts of data. The student will have tools to describe models with uncertainty and make decisions in the presence of this uncertainty.
Relationship with the profession
Statistics is a cross-cutting subject in a wide variety of disciplines, from physics, chemistry to the social sciences. In recent decades, quality control has brought statistics to practically all companies and is used for decision making in almost all business areas. In IT, its use is very common for preparing reports and its use is very frequent in topics such as Data Mining where there is a growing number of IT professionals working. At the level of consultants, any consultant must have basic knowledge of statistics, just as any computer analyst must know techniques based on inference.
Course competences | |
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Code | Description |
BA01 | Ability to solve mathematical problems which can occur in engineering. Skills to apply knowledge about: lineal algebra; integral and differential calculus; numerical methods, numerical algorithms, statistics, and optimization. |
INS01 | Analysis, synthesis, and assessment skills. |
PER01 | Team work abilities. |
SIS01 | Critical thinking. |
SIS03 | Autonomous learning. |
UCLM02 | Ability to use Information and Communication Technologies. |
UCLM03 | Accurate speaking and writing skills. |
Course learning outcomes | |
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Description | |
Use of proper terms in statistics, as well as resoning methods in several real situations. | |
Selection of appropriate statistics tools for the analysis of several types of data depending on their type and source. | |
Use of statistics software for data analysis and extraction of numerical and graphical signs which summarize relevant information. | |
Additional outcomes | |
Not established. |
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | BA01 | 0.9 | 22.5 | N | N | ||
Individual tutoring sessions [ON-SITE] | Guided or supervised work | BA01 | 0.18 | 4.5 | N | N | ||
Other off-site activity [OFF-SITE] | Practical or hands-on activities | BA01 INS01 PER01 | 0.6 | 15 | N | N | ||
Study and Exam Preparation [OFF-SITE] | Self-study | BA01 INS01 | 2.1 | 52.5 | N | N | ||
Writing of reports or projects [OFF-SITE] | Self-study | BA01 INS01 PER01 | 0.9 | 22.5 | Y | N | ||
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | BA01 INS01 PER01 SIS01 SIS03 UCLM02 UCLM03 | 0.6 | 15 | Y | N | ||
Laboratory practice or sessions [ON-SITE] | Practical or hands-on activities | BA01 PER01 SIS01 SIS03 UCLM02 UCLM03 | 0.42 | 10.5 | Y | Y | ||
Final test [ON-SITE] | Assessment tests | BA01 INS01 SIS01 UCLM02 UCLM03 | 0.3 | 7.5 | Y | Y | ||
Total: | 6 | 150 | ||||||
Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
Total credits of out of class work: 3.6 | Total hours of out of class work: 90 |
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 |
Theoretical papers assessment | 15.00% | 15.00% | Non-compulsory activity that cannot be retaken. To be carried out before end of teaching period |
Laboratory sessions | 20.00% | 20.00% | Compulsory activity that can be retaken. To be carried out during lab sessions |
Final test | 55.00% | 55.00% | Compulsory activity that can be retaken. To be carried out at the date of the final exams of the ordinary course. |
Assessment of active participation | 10.00% | 10.00% | Non-compulsory activity that cannot be retaken. To be carried out during the theory/lab sessions |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
Individual tutoring sessions [PRESENCIAL][Guided or supervised work] | 4.5 |
Writing of reports or projects [AUTÓNOMA][Self-study] | 22.5 |
Final test [PRESENCIAL][Assessment tests] | 7.5 |
Unit 1 (de 5): Introduction to Statistics | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 3.5 |
Unit 2 (de 5): Descriptive Statistics | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 6 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 13 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 3 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 3 |
Unit 3 (de 5): Probablity Theory | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2.5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 12 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 2 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 2 |
Unit 4 (de 5): Random Variables and Probability distributions | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 6 |
Other off-site activity [AUTÓNOMA][Practical or hands-on activities] | 7 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 12 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 5 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 2 |
Unit 5 (de 5): Inferential Statistics | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 6 |
Other off-site activity [AUTÓNOMA][Practical or hands-on activities] | 8 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 12 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 5 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 3.5 |
Global activity | |
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Activities | hours |
General comments about the planning: | The subject is taught in three weekly sessions of 1.5 hours. |
Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
---|---|---|---|---|---|---|---|---|---|
Fernández Guerrero, Mercedes | Manual de estadística para ingenieros | Casa Ruiz Morote | 84-934398-2-8 | 2007 | |||||
Gómez, Arriaza | Estadística Básica con R y R- Commander | UCA | 978-84-9828186-6 | 2008 | http://knuth.uca.es/ebrcmdr | ||||
Montgomery, Douglas C. | Applied statistics and probability for engineers | John Wiley & Sons | 978-1-118-74412-3 | 2014 | |||||
Novo Sanjurjo, Vicente | Estadística teórica y aplicada | Sanz y Torres | 84-96094-30-8 | 2004 | |||||
Nájera López, Alberto | Sobrevivir a la estadística en 40 páginas y con 7 ejercicios | 2014 | |||||||
Walpole, Ronald E. | Probabilidad y estadística para ingenieros | Prentice-Hall Hispanoamericana | 970-17-0264-6 | 1999 | |||||
Álvarez Contreras, Sixto Jesús | Estadística aplicada : teoría y problemas | CLAG | 84-921847-4-4 | 2000 |