Course description
Big Data for Agri-Food: Principles and Tools
Agri-food deserves special focus when it comes to choosing robust data management technologies due to its inherent variability and uncertainty. Wageningen University Researchs knowledge domain is healthy food and the living environment. That makes our data experts especially equipped to forge the bridge between the agri-food business on the one hand, and data science, artificial intelligence (AI) on the other.
Combining data from the latest sensing technologies with machine learning/deep learning methodologies, allows us to unlock insights we didn't have access to before. In the areas of smart farming and precision agriculture this allows us to:
- Better manage dairy cattle by combining animal-level data on behaviour, health and feed with milk production and composition from milking machines.
- Reduce the amount of fertilisers (nitrogen), pesticides (chemicals) and water used on crops by monitoring individual plants with a robot or drone.
- More accurately predict crop yields on a continental scale by combining current with historic data on soil, weather patterns and crop yields.
Upcoming start dates
Suitability - Who should attend?
Prerequisites:
A university education and/or working knowledge of math and science and, of course, being a computer science enthusiast will help a lot!
Outcome / Qualification etc.
What you'll learn
- Recognize big data characteristics (volume, velocity, variety, veracity)
- The difference between scaling up and scaling out
- Big data principles: immutability and pure functions
- Processing big data with map-reduce, using clusters
- Understand technologies: distributed file systems, Hadoop
- How dataframes and wrapper technology (Apache Spark) make life easier
- The big data workflow and pipeline
- How data is organized in datalakes, using lazy evaluation
- Develop insight how to apply this to your own case
Training Course Content
Big data definition and characteristics
In module 1, you will learn how to recognize the characteristics of a big data problem in agriculture, to see where its biggest challenge lies. Should the solution focus on size, speed, various formats or uncertainty of data? Should you scale up or scale out?
Big data principles: what are they and why do we need them
In module 2, you'll learn the principles that are required for scaling out: immutability and pure functions, and map-reduce. What are these and why do we need them?
Bring those principles to practice
Module 3 shows you how to bring those principles into practice. You will learn what a cluster is, and how a distributed file system in a client-server architecture works, with Hadoop. You will understand why such a system is indeed scalable.
Big data technologies that make implementation so much easier
Module 4 goes further into the application of big data technology, the big data stack of technologies". The main message here is that if you know what you want to do, these technologies can take the work out of your hands. For example, you will see Apache Spark, a big data technology platform, that applies map-reduce for you.
The big data workflow and pipeline; the how and why of datalakes
Module 5 dives deeper into the data. You'll learn about datalakes and why a datalake is different from a traditional database. You'll understand what a big data workflow looks like and what a pipeline is.
Course delivery details
This course is offered through Wageningen University & Research, a partner institute of EdX.
6-10 hours per week
Expenses
- Verified Track -$169
- Audit Track - Free