OBJECTIVE OF THE COURSE

The overall aim of the course is to proporcionar al alumnado los fundamentos para la comprensión y uso del Monitoring system of the Food and Nutritional Security de cara a la toma de decisiones apoyadas en la información contenida en la plataforma digital.

BLOCK 1: Aporte de PREDISAN  a los factores que contribuyen a la SAN

1.1. Session 1

• Introduction to the PREDISAN platform (Goals, functioning, iputs and outputs)
• Data sources (socio-economic, agroclimátic, (SAN) data field.
• Aligment with the CIF/IPC methodology.

1.2. Session 2

• Vulnerability factors (health indicators, agro-climatic / natural disasters, socio-economic, vulnerability profiles).
• Hazards (Agro-climatic / natural disasters, socio-economic and others).
• Results (first level and second level)
• Impact (food availability, food access, food use).
• Predictions
• Report space
• Decision-making assistance in relation to the CIF / IPC processes.

BLOCK 2: PREDISAN management.

2.1. Session 3

• How to share data to be included in PREDISAN?
• Quality standards for data entered into PREDISAN.
• PREDISAN governance.
• Updating of data on the platform.

BLOCK 3: Data analysis and predictive models.

3.1. Session 4

• Data processing.
• What are ETL processes?
• Data science. Objectives and applications.
• Predictive models
  • What is a predictive model (nowcasting vs forecasting)?
  • Aprendizaje automático frente a inteligencia artificial.
  • How are predictive models built?
  • How is the quality of a predictive model validated?
  • Types of predictive models (classification / regression).
  • What predictions are currently in PREDISAN?
  • What predictions do we want to achieve in the future at PREDISAN?
  • Possible scope for improvement and reorientation of the approach.

Evaluable form

• As an evaluable activity, a questionnaire with general knowledge questions on Data Science and on the information contained in PREDISAN is proposed.

Jorge Hernández Marín

Engineer of Roads, Canals, and Ports at the University of Granada, GIS specialist by the master UNIGIS of the University of Girona and the co-Founder and CEO of GIS4tech S.L.

Ronny Adrián Meza Acosta

Geophysical Engineer, Master in Information Science and Computer Engineering and Master in Data Science and Computer Engineering. 

The methodology of the course is as follows online, the subjects will be explained in a clear and detailed way, by means of didactic manuals in multimedia format, live classes (videoconferences) to explain the key points of the syllabus, resolve doubts and clarify concepts, where teachers and students interact in a continuous exchange of knowledge and resolution of doubts.

It is proposed to study the contents in a synchronous way, allowing participants to learn at their own pace. The contents of the course are contained in a PDF document that can be downloaded by students. Also, examples of solved exercises will be provided in relation to the topics taught throughout the course. In addition, there will be a tutoring system through the chat activated within the learning platform. The course will be run by a teacher, who will answer any questions you may have about the course and who will liaise with the teachers for specific questions about each module.

The course presents a combination of varied resources with the aim of facilitating the learning of key concepts that allow the application of the knowledge acquired.

SAN managers of the humanitarian field interested in analyzing and visualizing information.

Students will be asked to complete an evaluable form, which must be completed by the students within the established deadline, and which must be satisfactorily passed.

In the course material, students will find a set of data, which will be used with the aim of replicating the activities they will carry out in real cases. This will allow them to reinforce their knowledge and help them to clarify any doubts that may arise when replicating the practical exercises.