First chance evaluation

There will be a multiple-choice test of 10 to 20 questions that represents 40% of the grade. On the other hand, the continuous assessment will consist of attendance and / or delivery of practices related to learning and application of statistical software R for problem solving in the field of digital information management (20% of the overall grade), in addition to the delivery of one and / or several works of application of statistics for the resolution of case studies in digital documentation (alternatively may be tasks of revision or extension of the subject) which represents 40% of the final grade.

Second chance evaluation

The evaluation will be done following the same procedure as in the first opportunity.

Early exam session

All these remarks are applied to the early exam session.

"No presentado" grade

For any of the two opportunities to pass the subject, the "NO PRESENTADO" grade will be given to the students who did non take the objective final test.

Students with recognition of part-time dedication and/or academic exemption of attendance

In the case of students with recognition of part-time dedication and/or academic exemption of attendance that decides not to attend classes, they will be evaluated in the two opportunities as the rest of the students who are in a similar situation.

Fraud in tests or evaluation activities will directly imply the failure grade "0" in the subject in the corresponding call, thus invalidating any grade obtained in all the evaluation activities for the extraordinary call.

Learning outcomes	Study programme competences
To know the most common descriptive measures and graphical representation of data.	A8 A13 A21	B1 B8 B9
Ability to synthesise and analyse a set of data descriptively and graphically.	A8 A21 A22	B2 B3 B4 B5 B8 B9
To know the concept of probability, rules of probabilistic calculation and the most common probabilistic models.	A1 A21	B1 B2 B3 B4 B5
Ability to apply computer tools of statistical analysis for decision-making and for the development and exploitation of information systems.	A22	B11	C2 C6
To integrate theoretical and practical statistical knowledge as a pathway to knowledge and reflective and holistic thinking.	A1 A5	B2 B3 B4 B5 B6 B7	C4 C7 C8
Capacity for analysis and synthesis applied to the management and organisation of information.		B2 B3 B4 B5 B6 B7 B8 B9	C1 C3 C5

Topic	Sub-topic
The following topics develop the contents established in the file of the Verification Report, being:	Sources of information and statistical methodology. Introduction to the use of statistical analysis programs (R software). Univariate descriptive statistics. Descriptive statistics of two variables. Analysis of dependence between variables and regression between statistical variables. Introduction to time series and index numbers. Methods for outlier detection (outliers). Probability. Random variables and probability distributions.
Introduction to statistics and sources of information	Introduction and purpose of statistics. Major concepts of statistics and data science. Data processing methodologies and main problems they address. Sources of statistical information. Organization of official statistics at national and international level (ONU agencies, Eurostat, INE, and IGE, among others). Main statistics in the socioeconomic field (demographic, social, employment, economic statistics). Digital information sources.
Descriptive statistics of a unidimensional variable.	General concepts. Frequencies tables. Measures of position, dispersion and shape. Graphical representation of unidimensional variables.
Descriptive statistics of more than one variable.	Descriptive statistics of two variables. Measures of position and dispersion. Graphical representation of multivariate data. Dependence relationship between qualitative variables. Dependence relationship between quantitative variables: simple linear regression. Other regression models. Descriptive methods of unsupervised classification by clusters and for the detection of outliers.
Introduction to the use of programs for the statistical analysis of data (R software).	Description of the statistical software R. Structure of R. Introduction to programming with R. Functions. Object definition. Assignment. Creating and importing databases. Obtaining graphs. Elaboration of reports.
Introduction to time series and index numbers.	Indicators in libraries and the field of documentation. Bibliometric indexes. Index numbers. Simple and composite index numbers. Introduction to descriptive analysis of time series.
Probability	Basic concepts. Operations with events. Laplace's rule. Properties of probability. Conditional probability. Product rule, rule of total probabilities, Bayes' rule. Applications to documentation problems.
Random variables	Definition of a random variable. Discrete random variables. Continuous random variables. Probability mass function. Density function. Distribution function. Mean, variance, calculation of probabilities and quantiles.
Probability distributions	Binomial distribution. Hypergeometric distribution. Negative binomial distribution. Normal distribution. Uniform distribution. Exponential distribution. Distributions associated to the normal distribution. Other distributions.

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
ICT practicals	A13 A22 B11 C2	12	0	12
Guest lecture / keynote speech	A1 A5 A8 A21 B1 B3 B7 C4 C7	21	0	21
Supervised projects	B2 B4 B5 B6 B8 B9 C1 C3 C5 C6 C8	1.02	100.98	102
Objective test	A21 B1 B2	1	0	1
Case study	A1 A8 A21 B2 B3 B4 B5 B6 B7 B8 B9 C1 C8	7	7	14

Personalized attention		0		0

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
ICT practicals	In the practical classes the student will be introduced to the handling of the statistical software R. Computational tools for the resolution of problems will be shown and applied through the statistical analysis of data, either from simulated or real data.
Guest lecture / keynote speech	Keynote speech will be given in which the teacher will explain, with the help of appropriate audiovisual media, the main contents of the subject.
Supervised projects	Students will be proposed to develop a group work (2 to 4 people) consisting of the application of statistical and computational tools shown in class to a particular case study, described by real or simulated data. You can also perform a work consisting of the description of a case study in the field of communications and information sciences, in which the resolution of a real problem is carried out based on the application of statistical techniques. A review study on a specific topic of the subject or the software used may also be carried out. The works can be proposed by the teachers or by the students themselves (the proposals will be taken into account or not always according to the teacher's criteria).
Objective test	It will consist of a multiple-choice test on the contents taught in the course.
Case study	The statistical techniques taught in the course will be applied to solve exercises and real and simulated case studies in the field of digital information management.

Methodologies	Competencies	Description	Qualification
Supervised projects	B2 B4 B5 B6 B8 B9 C1 C3 C5 C6 C8	These works will be carried out in groups of 2 to 5 people, applying statistics to real or simulated data, reviewing a topic on statistics or data science or even regarding a specific application of statistics related to the field of communication and information scienses.	40
ICT practicals	A13 A22 B11 C2	The attendance and performance of the student in the practical classes will be evaluated, as well as the delivery of works related to the application of the statistical software R.	20
Objective test	A21 B1 B2	It will consist of 15 to 20 test questions with three possible answers.	40

Assessment comments
First chance evaluation There will be a multiple-choice test of 10 to 20 questions that represents 40% of the grade. On the other hand, the continuous assessment will consist of attendance and / or delivery of practices related to learning and application of statistical software R for problem solving in the field of digital information management (20% of the overall grade), in addition to the delivery of one and / or several works of application of statistics for the resolution of case studies in digital documentation (alternatively may be tasks of revision or extension of the subject) which represents 40% of the final grade. Second chance evaluation The evaluation will be done following the same procedure as in the first opportunity. Early exam session All these remarks are applied to the early exam session. "No presentado" grade For any of the two opportunities to pass the subject, the "NO PRESENTADO" grade will be given to the students who did non take the objective final test. Students with recognition of part-time dedication and/or academic exemption of attendance In the case of students with recognition of part-time dedication and/or academic exemption of attendance that decides not to attend classes, they will be evaluated in the two opportunities as the rest of the students who are in a similar situation. Fraud in tests or evaluation activities will directly imply the failure grade "0" in the subject in the corresponding call, thus invalidating any grade obtained in all the evaluation activities for the extraordinary call.

Basic	Marín, J. (1999). Estadística Aplicada a las Ciencias de la Documentación. Murcia: Diego Marín Editor Judit Bar-Ilan (2008). Informetrics at the beginning of the 21st century—A review.. Journal of Informetrics Cao, R., Francisco, M., Naya, S., Presedo, M.A., Vázquez, M., Vilar, J.A. y Vilar, J.M. (2001). Introducción a la Estadística y sus aplicaciones.. Pirámide Egghe, L. y Rousseau, R. (1990). Introduction to Infometrics. Quantitative Methods in Library, Documentation and Information Science.. Amsterdam: Elsevier. Cao, R., Labora, A., Naya, S. e Ríos, M. (2001). Métodos estatísticos e numéricos. A Coruña: Baía José María Sarabia Alegría , Faustino Prieto Mendoza , Vanesa Jordá Gil (2018). Prácticas de estadística con R. Pirámide María Dolores Ugarte, Ana F. Militino, and Alan T. Arnholt (2012). Probability and Statistics with R. Springer Stephen, P. and Hornby, S. (1997). Simple statistics for library and information professionals.. London:Library Association Publishing Moya, F., López, J. y García C. (1996). Técnicas Cuantitativas Aplicadas a la Biblioteconomía y Documentación. Madrid: Síntesis

Complementary	Gonick, L. e Smith, W. (2001). A estatística ¡en caricaturas!. Lugo:SGAPEIO Cástor Guisande, Antonio Vaamonde (2012). Gráficos estadísticos y mapas con R. Díaz de Santos Judit Bar-Ilan (2008). Informetrics at the beginning of the 21st century—A review.. Journal of Informetrics