Data Science in Agrofood
Do you want to learn how to increase sustainability by applying data science in the broad discipline of crop science, livestock production and leisure
Expand your field of expertise in projects, examples are:
-Combine location and activity data for the early detection of claw disease of cows;
-Compare soil sensor data to increase yield and efficiency of crop farming;
-Analyse climate data of pig farms to improve health and welfare of the pigs;
-Use data of a potato farm using precision techniques and develop business models for special fries.
Data Science in Agrofood provides a learning environment for students with various backgrounds in which they acquire and apply knowledge and gain skills for developing innovative solutions, using data science and technology in the agrofood sector.
Themes to be discussed:
-new data science applications in smart farming and leisure;
-cross overs: gaining knowledge and skills from a multi-disciplinary perspective to find solutions for your own field of expertise;
Why sign up for this course?
You will be prepared for the future of Smart Farming by:
-working in a multidisciplinary project;
-looking at technical and biological aspects;
-handling complex data problems and offering practical solutions;
-being inspired by innovative precision technology and new data science solutions;
-following a diverse program of (guest) lectures from experts;
-developing your talents and showing your fellow students your own field of expertise.
· 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.
Workshops and lectures
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 agriculture, food products as well as data science.
1 Project report and presentation (50%)
2 Oral assessment (25%)
3 Written test(s) (25%)
'May be subject to change'
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.