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Data science in Agrifood AVANS/HAS

Data is a key asset in our economy and social environment. On a daily basis we see new initiatives to make more use of data. In this joined minor minor of HAS and Avans you will be working in a multidisciplinary team to explore today’s complex data related opportunities. You will be working on real business cases provided by external organizations. The projects will focus on the agrifood business domain.  

The focus of the minor is to discover, and gain experience in the application of data science in a real project situation.

The first two weeks focus on a global introduction on data science and in agrifood and to get to know the tutors and your fellow students.

The next period focusses on data science classes and agrifood domain classes. These should give you the entry-level to start the projects. You will practice your new skills in various small assignments.

In week 6 you will select the project you will be working on for the remainder of the minor. Together with your fellow students you form a multi-disciplinary team to work on the project.

From this moment onwards your week consists of:

  • Working on your project to reach the agreed goals (70 %)
  • Deep diving in the agrifood domain and data science by following classes (15%)
  • Working on achieving your individual learning objectives which you base on the project needs and your personal interests (15%)

During your project and while working on your individual learning objectives you will be coached by lecturers of the HAS University of Applied Sciences and Avans University of Applied Sciences. 

The minor data science in agrifood will consist of the following modules: 

Data analysis / data visualization 

Project management workshops 

Data science introduction

Python for data science 

Data Governance / master data management 

Architecture technology 

Domain knowledge of the agrifood sectors

Individual learning objectives

Project 

The feedback by last year’s students of this minor was that students gained good insight in what data science is (3.8 on a scale from 1-5) and gained good understanding if they would like to work in data science initiatives after graduating (3.5 on a scale from 1-5).

As we continue improving  the minor based on students feedback the weeknumbers and percentages mentioned above are estimates at this moment in time.

This minor is a cooperation between Avans and HAS 

 

Leerdoelen

  • The student can value current precision sensor systems and data science techniques and their application (datascience) in the field of agrofood and come up with arguments and ideas for alternatives, so that he can be a true equal in consultations and discussions in the field of smart farming
  • The student is able to form a vision on how smart farming can be used to create an international/social sustainable system in the future
  • The student can reflect on one’s personal performance and progress in knowledge and skills in datascience and the field of smart farming, so that he can constantly develop himself
  • The student can use existing algorithms and understands the underlying statistics in such a way that he can adequately solve a problem
  • The student can recognize the data components of a business problem and offer a solution using data science techniques.
  • The student can talk to the client and analyze the client’s real problem in a discussion with the client to touch upon the underlying questions and problems.
  • The student can collaborate with peers from similar and different disciplines in such a  way that he can integrate ideas logically in an advice
  • The student can recognize the smart farming components of a business problem and offer a solution using smart farming techniques.
  • The student can integrate knowledge and methods from other areas of expertise into his/her own (multidisciplinarity) in order to combine insights to solve problems
  • The student can explain the decisions and data science steps that lead to the advice to have a logical and substantiated advice.
  • The student uses communication theories and models and commercial skills to advise the client and reflect on the advisory process.

Ingangseisen

To participate in this minor, you must have completed your first year and have earned at least 40 ECTS during your second year. In addition, students should have affinity with data science and interest in the agrifood domain.

Rooster

This is a fulltime module. The schedule is not known yet. Lectures on data science will be in the AVANS building, guest lectures on agrofood and environment will be in the HAS building, both at the Onderwijsboulevard in Den Bosch.

Toetsing

The grade for the course Data Science in Agrifood will consist of several parts.

  1. Grade for the technical assessment on data science knowledge (20%)
  2. Grade for end assessment (20%)
  3. Grade for the project (40%)
  4. Grade for article on agrifood domain knowledge (20%)

For the project there is a threshold grade of 5.5.

For the article on article on agrifood domain knowledge there is a threshold grade of 5.5.

For the grade of the technical assessment and the grade of the oral assessment there is a threshold grade of 4.0 with a threshold of a weighted average of these two grades >=5.5.

Aanvullende informatie

For more information about this or other minors offered by Avans Academy of Applies Sciences, click here to check our minormagazine