| Study programme competencies |
| Code | Study programme competences |
| A2 | CE2 – To define, evaluate and select the architecture and the most suitable software for solving a problem in the field of bioinformatics |
| A3 | CE3 – To analyze, design, develop, implement, verify and document efficient software solutions based on an adequate knowledge of the theories, models and techniques in the field of Bioinformatics |
| A4 | CE4 - Ability to acquire, obtain, formalize and represent human knowledge in a computable form for the resolution of problems through a computer system in any field of application, particularly those related to aspects of computing, perception and action in bioinformatics applications |
| A6 | CE6 - Ability to identify software tools and most relevant bioinformatics data sources, and acquire skill in their use |
| B1 | CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research |
| B2 | CB7 - Students should know how to apply the acquired knowledge and ability to problem solving in new environments or little known within broad (or multidisciplinary) contexts related to their field of study |
| B3 | CB8 - Students to be able to integrate knowledge and deal with the complexity of making judgements from information that could be incomplete or limited, including reflections on the social and ethical responsibilities linked to the application of their skills and judgments |
| B6 | CG1 -Search for and select the useful information needed to solve complex problems, driving fluently bibliographical sources for the field |
| B7 | CG2 - Maintain and extend well-founded theoretical approaches to enable the introduction and exploitation of new and advanced technologies |
| C1 | CT1 - Express oneself correctly, both orally writing, in the official languages of the autonomous community |
| C3 | CT3 - Use the basic tools of the information technology and communications (ICT) necessary for the exercise of their profession and lifelong learning |
| C6 | CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Coñecer e comprender os paradigmas e aspectos máis relevantes do tratamento de bases de datos de alta dimensión | AJ2 AJ3 AJ4 AJ6 |
BJ1 BJ2 BJ3 BJ6 BJ7 |
CJ1 CJ3 CJ6 |
| Coñecer e saber aplicar os principais métodos de minería de datos; coñecer as plataformas e as paradigmas principais que se empregan no campo. | AJ2 AJ3 AJ4 AJ6 |
BJ1 BJ2 BJ3 BJ6 BJ7 |
CJ1 CJ3 CJ6 |
| Contents |
| Topic | Sub-topic |
| Introducción ao Big data. | Qué é Big Data Principais características do Big data Principais campos de aplicación |
| Minería de datos e alta dimensión | Analítica Big data Técnicas de preprocesado MapReduce |
| Modelos de programación Batch | Hadoop Resilient Distributed datasets Programación batch en Spark |
| Modelos de programación streaming |
Conceptos básicos Kafka, Apache Storm, Spark streaming |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A4 C1 C6 | 7 | 14 | 21 |
| Problem solving | A25 A33 A41 B1 B6 C3 | 8 | 16 | 24 |
| Supervised projects | A21 B3 B6 C1 C2 C3 C6 | 4 | 4 | 8 |
| Seminar | A21 B1 B3 B6 | 4 | 4 | 8 |
| Mixed objective/subjective test | A2 A3 A4 A6 B1 B2 B3 B6 B7 C1 C3 C6 | 4 | 10 | 14 |
| Personalized attention | 0 | 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 | Empregada durante as clases presenciais teóricas para expor o núcleo básico de coñecementos que logo os alumnos terán que saber utilizar e ampliar nas prácticas, seminarios e traballos do curso |
| Problem solving | Emprego de técnicas de minería de datos en alta dimensión. Uso de paradigmas Big data Realización dunha práctica nunha plataforma específica de Big data |
| Supervised projects | Entrega dun breve traballo que discutirase na clase sobre algún aspecto concreto da materia. |
| Seminar | Exposición dun traballo específico de investigación que involucre tecnoloxías de alta dimensionalidade |
| Mixed objective/subjective test | Realizarase ao final do cuadrimestre sobre os contidos tratados ao longo do curso. |
| Personalized attention |
|
|
| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Seminar | A21 B1 B3 B6 | Seminarios de temas específicos | 0 |
| Supervised projects | A21 B3 B6 C1 C2 C3 C6 | Nota correspondente á parte práctica da materia, que comprende tanto os desenvolvementos realizados sobre as plataformas, como os traballos entregados. | 50 |
| Mixed objective/subjective test | A2 A3 A4 A6 B1 B2 B3 B6 B7 C1 C3 C6 | Realizarase unha proba con cuestións relativas ás partes teóricas da materia | 50 |
| Guest lecture / keynote speech | A4 C1 C6 | Clases presenciais | 0 |
| Assessment comments | |||
| Sources of information |
| Basic |
Venkat Ankam (2016.). Big Data Analytics. Packt Publishing
Thilina Gunarathne (2015). Hadoop MapReduce v2 Cookbook. Packt Publishing
Tom White (2015). Hadoop: The Definitive Guide. O'Reilly Media
Vladimir Bacvanski. (2015). Introduction to Big Data An Overview of Fundamental Big Data Concepts, Tools, Techniques and Practices.. O'Reilly Media
Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia (2015). Learning Spark. O'Reilly Media
Sean T. Allen, Matthew Jankowski, and Peter Pathirana (2015). Storm Applied. . O'Reilly Media |
|
|
|
| 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 | |