| Study programme competencies |
| Code | Study programme competences |
| A17 | CE16 - Knowledge of the process and tools for processing and preparing data, from their acquisition, extraction, and cleansing to their transformation, loading, organisation and access |
| B2 | CG02 - Successfully addressing each and every stage of an AI project |
| B3 | CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B4 | CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
| B5 | CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making |
| B6 | CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 | CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 | CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| C3 | CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C7 | CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 | CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| C9 | CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Develop the capacity to analyse and model data for processing in intelligent systems. | AC16 |
BC6 BC7 |
CC3 CC9 |
| Know and understand the process of extraction, cleaning, transformation, load and preprocessing of data. | AC16 |
BC2 BC3 BC8 |
CC3 CC9 |
| Know and learn how to use multidimensional and NoSQL databases. | BC3 BC4 BC7 |
CC8 |
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| Know the foundations of data lakes and data warehouses. | BC2 BC5 BC7 BC8 |
CC3 CC7 CC8 |
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| Contents |
| Topic | Sub-topic |
| Conceptos e fundamentos de Enxeñaría de datos | Conceptos e definicións básicas, problemas de carga eficiente en escenarios Big Data, almacenamento de datos masivos e acceso aos mesmos. |
| Técnicas de limpeza e preparación de datos. | Técnicas máis comúns. Definición de fluxos de procesamento. Medidas de calidade. |
| Estruturas avanzadas e almacéns de datos eficientes para Big Data |
Data warehouses e BD multidimensionais, Data lakes, Bases de Datos NoSQL. |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | B4 B5 C3 C9 | 12 | 0 | 12 |
| Practical test: | A17 B2 B5 B7 C3 | 8 | 0 | 8 |
| Problem solving | A17 B2 B4 B7 C7 C9 | 0 | 50 | 50 |
| Supervised projects | A17 B2 B3 B6 B7 B8 C7 C8 | 5 | 0 | 5 |
| 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 |
| Methodologies | Description |
| Guest lecture / keynote speech | The teacher will introduce given subjects to the students with the aim to acquire information valuable within a specific scope. |
| Practical test: | Problem or problems of practical character whose resolution requires the understanding and application of the theoretical and practical contents covered by the course. The students can work the solution to the proposed problems individually or in groups. |
| Problem solving | A project whose scope and aims require that the students work autonomously, although under the supervision of the teachers. |
| Supervised projects | Practical projects whose scope requires a significant fraction of the total dedication of the student to the course. Besides, the scale of these projects requires that the students apply management skills in addition to technical skills. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Supervised projects | A17 B2 B3 B6 B7 B8 C7 C8 | Defense of the solution proposed by the student or oral presentation of the developed solution. |
30 |
| Practical test: | A17 B2 B5 B7 C3 | Several assessment tests will be conducted in order to evaluate the understanding of the knowledge exposed in the classes of theory and/or practical. These tests can not be repeated in the second evaluation call. |
30 |
| Problem solving | A17 B2 B4 B7 C7 C9 | The evaluation of the autonomous work will include the submission of a report and a defense in which the students explain their developments and conclusions in front of the teacher and the classroom. |
40 |
| Assessment comments | |||
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FIRST AND SECOND EVALUATION CALLS [Assisting and Non-assisting students] Final grade = 0,30 * Project based learning + 0,30 * Laboratory practical tests + 0,40 * Autonomous problem solving Non-assisting students will complete the same assignments and tests than assisting students. FINAL GRADES To pass the course in any of the evaluation calls, the final grade must be equal or greater than 5 (from a total of 10), obtaining a minimum score of 5 (out of 10) in each of the evaluation parts. In the second opportunity the laboratory practical tests cannot be repeated, so there is no minimum score in this part. ADDITIONAL REMARKS If plagiarism is detected in any of the works (essays or project), the final grade will be "Suspenso" (0) and the situation will be notified to the School's Board to take the appropriate disciplinary actions. If translation errors cause any contradictions between the various versions of this syllabus, the English will be the prevailing version. |
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| Sources of information |
| Basic |
Ihab F. Ilyas, Xu Chu, (2019). Data Cleaning. Association for Computing Machinery. ACM
Avi Silberschatz, Henry F. Korth, S. Sudarshan (2010). Database System Concepts. McGraw-Hill
Sadalage, Fowler (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley
Alex Gorelik (). The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science. O'Reilly |
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| Complementary |
Matt Casters, Roland Bouman, Jos van Dongen (2013). Pentaho Kettle Solutions: Building Open Source ETL Solutions with Pentaho Data Integration. Wiley |
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| 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 | |