Big Data for Agri-Food: Tools and Principles
Learn big data analytics for agriculture using Python, Hadoop, and Spark. Master data processing tools for food industry applications.
Learn big data analytics for agriculture using Python, Hadoop, and Spark. Master data processing tools for food industry applications.
This comprehensive course demystifies big data technologies for the agri-food sector. You'll learn fundamental principles of handling large datasets, focusing on scalable solutions and parallel processing. The course covers essential concepts like immutability and pure functions, while providing practical experience with Python, Hadoop HDFS, and Apache Spark. Using real-world examples from agriculture and food production, you'll explore how modern sensing technologies and machine learning can revolutionize farming practices. Topics include smart dairy management, precision agriculture, and crop yield prediction. The course bridges the gap between data science and agricultural applications, preparing you to implement effective big data solutions in your field.
4.4
(8 ratings)
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What you'll learn
Recognize and analyze big data characteristics including volume velocity variety and veracity
Understand the distinction between scaling up and scaling out in data processing
Master big data principles of immutability and pure functions
Implement map-reduce processing using computer clusters
Apply technologies like distributed file systems and Hadoop effectively
Utilize dataframes and Apache Spark for efficient data handling
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This comprehensive course bridges the gap between big data technologies and agricultural applications. Students learn fundamental principles of handling large datasets, with a focus on scalable solutions and parallel processing. The curriculum covers essential concepts like data characteristics, scaling strategies, immutability, and pure functions. Practical applications include working with distributed file systems, Hadoop, and Apache Spark. Real-world agricultural examples demonstrate how combining sensor technologies with machine learning can revolutionize farming practices. The course emphasizes understanding data organization in datalakes and implementing effective big data workflows.
Big data definition and characteristics
Module 1
Big data principles: what are they and why do we need them
Module 2
Bring those principles to practice
Module 3
Big data technologies that make implementation so much easier
Module 4
The big data workflow and pipeline; the how and why of datalakes
Module 5
Fee Structure
Instructors
1 Course
AI Pioneer in Agricultural and Environmental Sciences
Ioannis N. Athanasiadis serves as Professor of Artificial Intelligence and Data Science at Wageningen University & Research, where he leads innovative research combining AI with agricultural and environmental sciences. His academic journey began with a PhD in Electrical and Computer Engineering from Aristotle University of Thessaloniki, followed by work at the Dalle Molle Institute for Artificial Intelligence in Switzerland before joining Wageningen University. His research portfolio spans machine learning, big data analytics, and knowledge engineering, with particular focus on environmental informatics, digital twins, and precision agriculture. He has published over 100 scientific papers and leads several major European research projects including AgrifoodTEF, PHENET, and SmartDroplets. At Wageningen, he teaches advanced courses in machine learning and big data for agri-food systems while leading the WUR theme on data-driven discoveries in a changing climate. His contributions to the field have earned him numerous accolades, including recognition in the 2024 Stanford/Elsevier List of Top 2% Scientists in the World for artificial intelligence. He serves as founding managing editor of Socio-Environmental Systems Modelling and as an editor for Environmental Modelling and Software, while actively contributing to major AI conferences and journals. His work particularly focuses on developing AI solutions to address global challenges in food security, agriculture, and environmental sustainability, making him a key figure in the application of artificial intelligence to agricultural and environmental sciences.
1 Course
Data Science and Agricultural Technology Expert
Sjoukje Osinga serves as Assistant Professor in the Information Technology group at Wageningen University, where she combines expertise in artificial intelligence, data science, and agricultural systems. After studying Artificial Intelligence and Cognitive Science in Groningen and Leuven, she completed her PhD at Wageningen University focusing on agent-based modeling of knowledge management in the pig sector, including six months of field research in China studying information management among pig farmers and stakeholders.
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4.4 course rating
8 ratings
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