First opportunityThere will be a multiple-choice test of 10 to 20 questions that represents 40% of the grade. On the other hand, the continuous evaluation will consist of the attendance and/or delivery of practices related to the learning and application of the statistical software R for the resolution of problems in the field of digital information management (20% of the overall grade), in addition to the delivery of one or several works of application of statistics for the resolution of case studies in digital documentation (alternatively they can be works of revision or extension of the subject) that represents 40% of the total grade.Second opportunityIn the evaluation of the second opportunity, the same criteria corresponding to the first opportunity will be followed.Advanced callAll the previous observations are applicable to students who request the advanced call of the exam.No-show gradeIn any of the two annual opportunities a NON PRESENTADO will appear in those cases in which the student does not attend the official exam of the subject.Student with recognition of part-time dedication and academic dispensation of exemption from attendance.In the case of students with recognition of part-time dedication and academic dispensation of exemption from attendance who decide not to attend classes, they will be evaluated in both opportunities as the rest of the students who are in a similar situation.&nbsp;The fraudulent performance of the tests or evaluation activities will directly imply the grade of "0" in the subject in the corresponding call, thus invalidating any grade obtained in all evaluation activities for the extraordinary call.

Learning outcomes	Study programme competences / results
Acquisition of skills for the selection, treatment, management and analysis of information through data mining techniques.
Knowledge and skills for the application of the main classification techniques.
Acquire knowledge of computational data analysis, including programs such as R statistical software.
Choosing the quantitative techniques appropriate to the objectives for research, administration and management tasks.
Capacity for analysis and synthesis applied to the management and organization of information.
Knowledge and skills for the application of regression techniques, anomaly detection and time series.
Acquisition of decision-making skills based on statistical analysis of complex databases.
Knowledge and skills for the application of the main classification techniques.
Acquisition of decision-making skills based on statistical analysis of complex databases.
Knowledge and skills for the application of regression techniques, anomaly detection and time series.
Capacity for analysis and synthesis applied to the management and organization of information.
Choosing the quantitative techniques appropriate to the objectives for research, administration and management tasks.
Acquire knowledge of computational data analysis, including programs such as R statistical software.
Acquisition of skills for the selection, treatment, management and analysis of information through data mining techniques.
Acquisition of skills for the selection, treatment, management and analysis of information through data mining techniques.
Acquire knowledge of computational data analysis, including programs such as R statistical software.
Choosing the quantitative techniques appropriate to the objectives for research, administration and management tasks.
Capacity for analysis and synthesis applied to the management and organization of information.
Knowledge and skills for the application of regression techniques, anomaly detection and time series.
Acquisition of decision-making skills based on statistical analysis of complex databases.
Knowledge and skills for the application of the main classification techniques.

Topic	Sub-topic
Introduction to data mining.	Preliminary concepts. Types of data mining problems: description, classification, prediction, clustering, anomaly detection, etc. Types of learning: supervised and unsupervised.
Unsupervised classification or clustering methods.	Basic concepts. Hierarchical classification methods. Partitioning clustering methods. Case studies in information science and documentation.
Supervised classification methods.	Basic concepts. Main models of supervised classification or pattern recognition. Validation of classification models (how well do they predict?). Case studies in information science and documentation.
Advanced regression methods.	Introduction. Univariate and multivariate regression models. Selection of relevant variables. Validation of regression models (how well does it fit the data, how well does it make predictions). Case studies in information science and documentation.
Time series	Basic concepts. Descriptive time series analysis. Practical use of time series models. Case studies.
Statistical techniques for text mining and information retrieval.	Basic concepts. Practical cases of application of text mining.

Methodologies / tests	Competencies / Results	Teaching hours (in-person & virtual)	Student’s personal work hours	Total hours
Guest lecture / keynote speech		19	0	19
ICT practicals		13	0	13
Case study		7	7	14
Supervised projects		1	101	102
Objective test		1	0	1

Personalized attention		1	0	1

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

Methodologies	Description
Guest lecture / keynote speech	They will be expository sessions in which the various topics of the subject will be introduced and described through presentations (using appropriate audiovisual media) that will include theory and examples.
ICT practicals	Practical classes will be developed using statistical software, in which its programming and application will be introduced based on real and simulated cases.
Case study	The statistical techniques taught in the subject will be applied to solve exercises and real and simulated case studies in the field of digital information management.
Supervised projects	Individual and/or group work will be carried out, supervised by the teachers of the subject, in which the resolution, by means of the application of statistical techniques and R software, of practical exercises or particular case studies related to the field of communication and information sciences will be approached. A review study of a specific topic of the subject or of the software used may also be carried out. The works may 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 subject.

Methodologies	Competencies / Results	Description	Qualification
ICT practicals		Attendance and/or student performance with the statistical software will be assessed.	20
Objective test		A multiple-choice test consisting of a number of questions between 10 and 20 with 3 possible answers.	40
Supervised projects		Individual and/or group work will be carried out, supervised by the teachers of the subject, in which the resolution, by means of the application of statistical techniques and R software, of practical exercises or particular case studies related to the field of communication and information sciences will be approached. A review study of a specific topic of the subject or the software used may also be carried out. The works may 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).	40

Assessment comments
<p><b>First opportunity</b></p><p><b></b></p><p>There will be a multiple-choice test of 10 to 20 questions that represents 40% of the grade. On the other hand, the continuous evaluation will consist of the attendance and/or delivery of practices related to the learning and application of the statistical software R for the resolution of problems in the field of digital information management (20% of the overall grade), in addition to the delivery of one or several works of application of statistics for the resolution of case studies in digital documentation (alternatively they can be works of revision or extension of the subject) that represents 40% of the total grade.</p><p><b></b></p><p><b><strong>Second opportunity</strong></b></p><p><b></b></p><p>In the evaluation of the second opportunity, the same criteria corresponding to the first opportunity will be followed.</p><p><b></b></p><p><b><strong>Advanced call</strong></b></p><p><b></b></p><p>All the previous observations are applicable to students who request the advanced call of the exam.</p><p><b></b></p><p><b><strong>No-show grade</strong></b></p><p><b></b></p><p>In any of the two annual opportunities a NON PRESENTADO will appear in those cases in which the student does not attend the official exam of the subject.</p><p><b></b></p><p><b><strong>Student with recognition of part-time dedication and academic dispensation of exemption from attendance.</strong></b></p><p><b></b></p><p>In the case of students with recognition of part-time dedication and academic dispensation of exemption from attendance who decide not to attend classes, they will be evaluated in both opportunities as the rest of the students who are in a similar situation.&nbsp;</p><p><b></b></p><p>The fraudulent performance of the tests or evaluation activities will directly imply the grade of "0" in the subject in the corresponding call, thus invalidating any grade obtained in all evaluation activities for the extraordinary call.</p>

Basic	Williams, G. (2011). Data mining with Rattle and R: The art of excavating data for knowledge discovery. . Springer Science &amp; Business Media. Cirillo, A. (2017). R Data Mining: Implement Data Mining Techniques Through Practical Use Cases and Real-world Datasets. . Packt Publishing. Jockers, M.L. (2014). Text Analysis with R for Students of Literature. Springer Silge, J. y Robinson, D. (2017). Text Mining with R: A Tidy Approach. O'Reilly

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