The volume of available data has doubled in the past five years. This is creating new opportunities for business and science alike, and can assist the agri-food system to create more powerful processes

Organisation

Key reasons to attend this course

Learn about the methods and computational architectures used to give answers to previously unanswerable questions using Big Data analytics.

Better understand a range of analytical techniques that can be applied to big data including traditional statistical methods, machine learning and artificial intelligence.

Be aware of the issues that arise when transferring big data analytics to a production environment.

Gain experience in organising data using traditional methods, such as SQL, as well as more modern approaches including NoSQL and the Semantic Web.

Participate in real world examples of business applications of big data.

Lecturers

Applied approach
(lectures and practical work)

10 leading international experts

Course given in English

Programme

        Download programme         Download Timetable         Download brochure
09:00-    10:00

Opening

10:00-    11:00

Introduction to Big Data

G. Anzaldi

11:00-    11:30

Coffee break

11:30-    12:30

Introduction to Big Data

G. Anzaldi

12:30-    13:30

Accessing, organising and handling Big Data

M. Solanki

13:30-    15:00

Lunch break

15:00-    18:00

Practical work

Understand a relevant state of the art Big Data architecture and tools with a simple example
Accessing data from external sources
Data integration
Data exploration

M. Solanki, X. Domingo, L. Echeverría

09:00-    11:00

Accessing, organising and handling Big Data

M. Solanki

11:00-    11:30

Coffee break

11:30-    13:30

Accessing, organising and handling Big Data

M. Solanki

13:30-    15:00

Lunch break

15:00-    16:00

Analysing Big Data: Statistical and machine learning processes phases

F. van Eeuwijk

16:00-    19:00

Practical work

Consumer segmentation for targeted marketing

S. Coleman, X. Domingo, F. van Eeuwijk, L. Echeverría

09:00-    10:00

Analysing Big Data: Statistical and machine learning processes phases

F. van Eeuwijk

10:00-    11:00

Analysing Big Data: How do we improve our models?

F. van Eeuwijk

11:00-    11:30

Coffee break

11:30-    12:30

Analysing Big Data: How do we improve our models?

F. van Eeuwijk

12:30-    13:30

Analysing Big Data: Supervised learning

S. Coleman

13:30-    15:00

Lunch break

15:00-    16:00

Analysing Big Data: Unsupervised learning

S. Coleman

16:00-    19:00

Practical work

Predict plant growth from DNA and environmental conditions

F. van Eeuwijk, X. Domingo, L. Echeverría

09:00-    10:00

Analysing Big Data: Other learning techniques

X. Domingo

10:00-    11:00

Analysing Big Data: Optimisation techniques

X. Domingo

11:00-    11:30

Coffee break

11:30-    12:30

Analysing Big Data:
Combining models in ensembles
Business intelligence tools

X. Domingo

12:30-    13:30

Architecture needed to exploit the results

L. Echeverría

13:30-    15:00

Lunch break

15:00-    16:00

ICARDA's activities on Big Data

C. Biradar

16:00-    19:00

Practical work

Image recognition

X. Domingo, C. Biradar, L. Echeverría

09:00-    10:00

Digitalisation of agri-food systems

L. Echeverría

10:00-    11:00

Big data applications in the agri-food sector revisited
Case study Bayer

J. Betrán

11:00-    11:30

Coffee break

11:30-    12:30

Big data applications in the agri-food sector revisited
Case study Carrefour

S. Melero

12:30-    13:30

Big data applications in the agri-food sector revisited
Case study John Deere

D. Arrobas

13:30-    15:00

Lunch break

15:00-    17:30

Practical work

Open data and other public data sources

X. Domingo, F. van Eeuwijk, S. Coleman, C. Biradar, L. Echeverría

Train at an outstanding international institution

Registration

The course is targeted to different actors in the agrifood system, such as: data managers, professionals involved in precision agriculture and livestock farming, food processors and distributors, marketing and sales managers, members from the administration involved with data advising decision makers and final users, specialists from consultancy services, and R+D experts involved in data analytics. Participants are expected to have a quantitative background, and some familiarity with scripting languages (e.g. R, Python).

The course will be held at IAMZ-CIHEAM in Zaragoza from 17 to 21 June 2019.
Application deadlines:
8 April 2019 - if you need a visa or intend to apply for a grant to attend the course. The deadline will be extended for candidates not applying for a grant and not requiring a visa while places are available.

Registration fees for the course amount to 500 euro. This sum covers tuition fees only.
Candidates from CIHEAM member countries (Albania, Algeria, Egypt, France, Greece, Italy, Lebanon, Malta, Morocco, Portugal, Spain, Tunisia and Turkey) and from ICARDA Middle East and North Africa (MENA) partners may apply for a limited number of full or partial scholarships offered by the organizing institutions covering registration fees, travel and accommodation.
Candidates from other countries who require financial support should apply directly to other national or international institutions.

It is compulsory for participants to have medical insurance valid for Spain. Proof of insurance cover must be given at the beginning of the course. Those who so wish may participate in a collective insurance policy taken out by the Organisation, upon payment of the stipulated sum.

Mediterranean Agronomic Institute of Zaragoza

  Av. MontaƱana 1005, 50059 Zaragoza, Spain

  www.iamz.ciheam.org

 iamz@iamz.ciheam.org

 +34 976716000

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