| Study programme competencies |
| Code | Study programme competences |
| A1 | CE1 - Know and understand the theoretical and methodological principles of information and documentation management to apply them in their professional activity |
| A4 | CE4 - Master the foundations of the behavior of individuals in the search, recovery and use of information, taking as a point of reference the aspects of motivation, environment and context |
| A8 | CE8 - Master the different methods of representation of data, information and knowledge that ensure efficient recovery |
| A13 | CE13 - Know and master the techniques and regulations for the creation and authentication, meeting, selection, organization, representation, preservation, recovery, access, dissemination and exchange, and evaluation of resources and information services |
| A20 | CE20 - Master the bases to develop research activities using multidisciplinary methods and principles |
| A21 | CE21 - Possess knowledge of statistics and quantitative analysis of information |
| A22 | CE22 - Acquire computational skills and management of new ICT |
| B1 | CB1 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context |
| B2 | CB2 - Apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study |
| B3 | CB3 - Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments |
| B4 | CB4 - Know how to communicate their conclusions -and the knowledge and ultimate reasons that sustain them- to specialized and non-specialized audiences in a clear and unambiguous way |
| B5 | CB5 - Possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous |
| B6 | CG1 - Capacity for cooperation, teamwork and collaborative learning |
| B7 | CG2 - Capacity for reflection and critical reasoning |
| B8 | CG3 - Capacity for planning, organization and management of resources, information and operations |
| B9 | CG4 - Capacity for analysis, diagnosis and decision making |
| B10 | CG5 - Ability to work in an international and global context |
| B11 | CG6 - Ability to understand the importance, value and function of the Digital Information and Documentation Management in the current ICT society |
| C1 | CT1 - Express correctly, both orally and in writing, in the official languages ??of the autonomous community |
| C2 | CT2 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise of their profession and for learning throughout their lives |
| C3 | CT3 - Develop oneself for the exercise of a citizenship that respects democratic culture, human rights and the gender perspective |
| C4 | CT4 - Understand the importance of the entrepreneurial culture and know the means available to entrepreneurs |
| C5 | CT5 - Acquire skills for life and habits, routines and healthy lifestyles |
| C6 | CT6 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to offer proposals that contribute to a sustainable environmental, economic, political and social development |
| C7 | CT7 - Assess the importance of research, innovation and technological development in the socio-economic and cultural progress of society |
| C8 | CT8 - Have the ability to manage time and resources: develop plans, prioritize activities, identify criticisms, establish deadlines and comply with them |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Acquisition of skills for the selection, treatment, management and analysis of information through data mining techniques. | A8 A13 A20 A21 A22 |
B2 B4 B5 B6 B7 B8 B10 B11 |
C2 C4 C5 C6 C8 |
| Choosing the quantitative techniques appropriate to the objectives for research, administration and management tasks. | A1 A8 A13 A20 A21 A22 |
B2 B4 B5 B6 B7 B8 B10 B11 |
C2 C4 C6 C8 |
| Acquire knowledge of computational data analysis, including programs such as R statistical software. | A1 A4 A8 A13 A20 A21 A22 |
B1 B2 B3 B9 B11 |
C1 C3 C7 |
| Knowledge and skills for the application of the main classification techniques. | A1 A20 A21 A22 |
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 |
C2 C4 C6 C7 C8 |
| Knowledge and skills for the application of regression techniques, anomaly detection and time series. | A1 A20 A21 A22 |
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 |
C2 C4 C6 C7 C8 |
| Capacity for analysis and synthesis applied to the management and organization of information. | A1 A4 A8 A13 A20 A21 A22 |
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 |
C1 C2 C3 C4 C5 C6 C7 C8 |
| Acquisition of decision-making skills based on statistical analysis of complex databases. | A1 A4 A8 A13 A20 A21 A22 |
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 |
C1 C2 C3 C4 C5 C6 C7 C8 |
| Contents |
| 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. |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A1 A4 A8 A20 A21 B1 B3 B7 C4 C7 | 19 | 0 | 19 |
| ICT practicals | A13 A22 B11 C2 | 13 | 0 | 13 |
| Case study | A1 A8 A21 B2 B3 B4 B5 B6 B7 B8 B9 C1 C8 | 7 | 7 | 14 |
| Supervised projects | B2 B4 B5 B6 B8 B9 B10 C1 C3 C5 C6 C8 | 1 | 101 | 102 |
| Objective test | A21 B1 B2 | 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 |
| 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. |
| Personalized attention |
|
|
| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| ICT practicals | A13 A22 B11 C2 | Attendance and/or student performance with the statistical software will be assessed. | 20 |
| Objective test | A21 B1 B2 | A multiple-choice test consisting of a number of questions between 10 and 20 with 3 possible answers. | 40 |
| Supervised projects | B2 B4 B5 B6 B8 B9 B10 C1 C3 C5 C6 C8 | 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 | |||
|
First opportunity 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. Second opportunity In the evaluation of the second opportunity, the same criteria corresponding to the first opportunity will be followed. Advanced call All the previous observations are applicable to students who request the advanced call of the exam. No-show grade 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. 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. 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. |
|||
| Sources of information |
| Basic |
Williams, G. (2011). Data mining with Rattle and R: The art of excavating data for knowledge discovery. . Springer Science & 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 |
|
|
|
| Complementary | |
|
|
| Recommendations | ||
| Subjects that it is recommended to have taken before | ||
|
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |
|
To help to achieve a sustainable environment and meet the objective of action number 5: “Healthy and sustainable environmental and social teaching and research” of the "Green Campus Ferrol Action Plan": 1.- The delivery of the documentary works carried out in this subject: 1.1. It will be requested in virtual format and/or computer support. 1.2. It will be done through Moodle, in digital format without the need to print them. 1.3. If done on paper: -Plastics will not be used. - Double-sided prints will be made. - Recycled paper will be used. - Draft printing will be avoided. 2.- A sustainable use of resources and the prevention of negative impacts on the natural environment must be made. 3.- The importance of ethical principles related to the values ??of sustainability in personal and professional behavior must be taken into account. 4.- As it is included in the different regulations of application for university teaching, the gender perspective must be incorporated in this subject (non-sexist language will be used, bibliography of authors of both sexes will be used, intervention in student class will be encouraged and students...). 5.- We will work to identify and modify prejudices and sexist attitudes, and the environment will be influenced to modify them and promote values ??of respect and equality. 6. Situations of discrimination based on gender must be detected and actions and measures will be proposed to correct them. 7. The full integration of students who, due to physical, sensorial, psychic or sociocultural reasons, experience difficulties in an ideal, egalitarian and profitable access to university life will be facilitated. |