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MSc Agricultural Data Management & Decision Models
In Brief

The « Data Management and Decision Models » Master of Science is at the interface of cutting-edge technology and the agriculture and food industries. Topics of studiy include processing data from connected devices such as drones, satellites, tablets and smartphones and data on the behavior of stakeholders in a given field, and managing and analyzing big data for decision-making purposes.

It is widely believed that by 2020, big data will have had a revolutionary effect on companies in terms of management, research & development and marketing. The rapid development of digital technologies will result in the development of new approaches to agriculture, requiring new skills. The aim of the Master of Science is to train up data scientists for agriculture.Our course enjoys the support of several academic and industry partners, including the Institute of Soil Science and Plant Cultivation (IUNG) in Pulawy (Poland), ISAGRI, Cap Seine, Coop de France, Defisol, INVENTIV IT, Agro EDI Europe, TheGreenData and F4F.

Salima Taïbi, Head of the Modeling Department

 

Key information
  • duration : 18 months
  • admission : General engineering – Master’s degree or equivalent
  • campus location : Rouen
  • tuition fees : EUR 8,000
  • start of term : October 2, 2017
The curriculum
Objectives

A Master of Science with a strong focus on innovation and data science for agriculture.

  • Interdisciplinary teaching in English: agriculture, big data, mapping, data mining, computing, machinery, project management, sociology, etc.
  • Jobs centered on new technologies
  • Innovation with a project led by professionals
  • Expert supervision by professionals and researchers
Skills
  • The focus is on expertise in big data management, which has become a key factor in company performance and growth.
  • The course is centered around the acquisition of IT and statistics techniques, data mining and machine learning applied to agriculture and the food industry in general, with a special focus on precision agriculture.
  • Developing a good command of statistics software and programming languages will be a real strength for students interested in research and development in areas such as plant nutrition and plant and animal epidemiology.

All lectures are taught in English   ⋅   Some lectures in collaboration with the Polish Institute of Soil Science and Plant Cultivation (IUNG)   ⋅   6-month internship in industry   ⋅   Big data for companies project – Thesis

Agriculture and advent of Big Data
  • An overview of agriculture
  • Sources and reliability of data in several agricultural sectors
Data quality management in agriculture
  • Data cleaning / preprocessing / sampling strategy for big data
  • Applied statistics
  • Simulation methods
Data analysis applied to agriculture
  • Principal component analysis
  • Cluster analysis
  • Factorial analysis
  • Discriminant analysis
  • Software analysis (R, SPSS, etc.)
Survey methods
  • Mathods of collecting data
  • Unstructured data
  • Multiple correspondence analysis
  • Survey data analysis
  • Text mining: social network analysis, etc.
Software engineering
  • Software architectures: client-server, MVC, cloud computing, SOA, REST, microservices
  • Software development: algorithmic, object-oriented programming, HMI, macro
IT Big Data management
  • Data sources in agriculture: sensors, communications protocols / networks, data exchange standards in agriculture, open data, web of data
  • Database design and modeling: relational, XML, multidimensional, OLAP, compared with NoSQL/New SQL databases, query languages
Cross fields
  • Project management
  • Rural sociology
  • French language
  • English language
Agriculture
  • Mechanized agriculture
  • Micro parcels experimental designs
  • Precision agriculture
  • Decision-making tools
Machine learning methods
  • Cross validation method
  • Neural networks
  • K-means
  • Regression trees, bagging
  • Support vector machine
  • Random forests
  • Kernel methods
  • K-nearest neighbors method
  • Sparse methods for high-dimensional data
Big Data management II
  • Distributed fils systems, Hadoop
  • Parallel, distributed, massive data processing with Map Reduce
  • NoSQL/NewSQL databases
  • IT security for big data: vulnerabilities, protection-privacy/security policies, cryptography
Quantitative image analysis
  • Signal image processing
  • Mapping, learning QGIS software
Modeling
  • General linear method
  • Non-linear method
  • Time series modeling
Cross fields
  • IS strategy / management, system integration
  • French language (FLE)
  • English language
  • General engineering – Master’s degree or equivalent.
  • Exceptionally, students with a Bachelor’s degree or equivalence with experience.
  • Selection shall be based on the application and an interview.

 

Download the application form (PDF)

Return by :

  • April 28, 2017
  • June 30, 2017
Registration fees

Tuition fees for 18 months : EUR 8,000

Start of term

October 2, 2017

Career opportunities
Sectors

Data scientist / data miner

Chief data officer

Master data management

Epidemiologist (animals and plants)

Data designer

Agricultural machinery designer

Consultant

Research leader in agronomy

Manager of animal health observatories

Data / business analyst

Designer / developer