Big Data for Agri-Food: Principles and Tools from edX

Demystify complex big data technologies Compared to traditional data processing, modern tools can be complex to grasp. Before we can use these tools effectively, we need to know how to handle big data sets. You will understand how and why certain principles – such as immutability and pure functions – enable parallel data processing (‘divide and conquer’), which is necessary to manage big data.

During this course you will acquire this principal foundation from which to move forward. Namely, how to recognise and put into practice the scalable solution that’s right for your situation.

The insights and tools of this course are regardless of programming language, but user-friendly examples are provided in Python, Hadoop HDFS and Apache Spark. Although these principles can also be applied to other sectors, we will use examples from the agri-food sector.

Data collection and processing in an Agri-food context Agri-food deserves special focus when it comes to choosing robust data management technologies due to its inherent variability and uncertainty. Wageningen University & Research’s 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.

In short, this course’s foundational knowledge and skills for big data prepare you for the next step: to find more effective and scalable solutions for smarter, innovative insights.

For whom?You are a manager or researcher with a big data set on your hands, perhaps considering investing in big data tools. You’ve done some programming before, but your skills are a bit rusty. You want to learn how to effectively and efficiently manage very large datasets. This course will enable you to see and evaluate opportunities for the application of big data technologies within your domain. Enrol now.

This course has been partially supported by the European Union Horizon 2020 Research and Innovation program (Grant #810 775, “Dragon”).

What's inside

Learning objectives

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

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

Syllabus

Module 1: Big data definition and characteristicsIn 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?

Module 2: Big data principles: what are they and why do we need themIn 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?

Module 3: Bring those principles to practiceModule 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.

Module 4: Big data technologies that make implementation so much easierModule 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.

Module 5: The big data workflow and pipeline; the how and why of datalakesModule 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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces fundamental knowledge of big data technology allowing learners to enhance their skills and potentially advance their career

Emphasizes hands-on learning by providing examples using Python, Hadoop HDFS, and Apache Spark

Provides a strong foundation for understanding key principles and technologies of big data, which is highly sought by industries using this technology

Features instructors with expertise in agri-food, offering practical and relevant examples for learners in this sector

Emphasizes core principles that enable scalability, a key aspect of managing big data effectively

Bridge the gap between agri-food business and data science, making the course particularly relevant to those working in this field

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Big Data for Agri-Food: Principles and Tools with these activities:

Review big data concepts

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Review the foundational concepts of big data, such as volume, velocity, variety, and veracity, to strengthen your understanding of the course material.

Browse courses on Big Data

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Read assigned course materials
Review previous notes and assignments
Complete practice questions or exercises

Infographic on Big Data Characteristics

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Create an infographic that visually represents the key characteristics of big data, aiding in the comprehension and retention of this foundational concept.

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Gather and organize data points
Design the infographic using creative tools
Write clear and concise descriptions

Participate in big data discussion forums

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Engage with fellow students in discussions to share ideas, ask questions, and gain diverse perspectives on big data concepts and applications.

Browse courses on Big Data

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Join online discussion forums
Participate in discussions
Share your insights and respond to others

Four other activities

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Show all seven activities

Solve big data practice problems

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Engage in hands-on practice by working through problems and exercises related to big data processing and technologies.

Browse courses on Big Data Processing

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Find practice problems or exercises online
Solve problems independently
Check answers and identify areas for improvement

Explore big data technologies through tutorials

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Enhance your understanding of big data technologies by following guided tutorials and experimenting with practical examples.

Browse courses on Big Data Technologies

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Find tutorials on specific technologies or topics
Follow the tutorials and complete exercises
Apply what you've learned to your own projects

Develop a data analysis pipeline

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Create a data analysis pipeline to automate the processing and analysis of large datasets, reinforcing the principles and techniques covered in the course.

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Define the data sources and types
Design the pipeline architecture
Implement the pipeline using appropriate tools
Test and validate the pipeline
Deploy and monitor the pipeline

Develop a big data solution proposal

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Apply your knowledge and skills to develop a comprehensive proposal for a big data solution, showcasing your understanding of the concepts and their practical implementation.

Browse courses on Big Data Analytics

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Identify a real-world problem or challenge
Research and analyze potential big data solutions
Design and develop a solution proposal
Present your proposal

Career center

Learners who complete Big Data for Agri-Food: Principles and Tools will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data Engineers build and maintain data pipelines that move data from source systems to target systems. They work with data architects and data scientists to design and implement data management solutions. Data Engineers typically have a strong background in computer science and data engineering. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

See salaries and explore the career path for Data Engineer

Data Architect

Data Architects design and build data management systems that meet the needs of their organization. They work with stakeholders to understand their data requirements and develop a data management strategy. Data Architects typically have a strong background in computer science and data engineering. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Data Scientist

Data Scientists lead a team to collect, clean, and analyze massive datasets. They use statistical modeling to test hypotheses and uncover trends that can be monetized. They write reports and present their findings to decision-makers. This course can help you build a foundation in big data technologies. It will give you an overview of the big data landscape and teach you how to work with big data tools. This course can help you develop the skills you need to be successful in this role.

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Business Analyst

Business Analysts use data to solve business problems. They work with stakeholders to understand their business needs and develop data-driven solutions. Business Analysts typically have a strong background in business and data analysis. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Market Researcher

Market Researchers collect and analyze data to understand market trends and customer behavior. They use this data to develop marketing campaigns and strategies. Market Researchers typically have a strong background in business and marketing research. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Operations Research Analyst

Operations Research Analysts use mathematical models to solve business problems. They work with businesses to improve their efficiency and productivity. Operations Research Analysts typically have a strong background in mathematics and operations research. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Financial Analyst

Financial Analysts use data to evaluate the financial performance of companies. They make recommendations to investors and other stakeholders on whether to buy, sell, or hold stocks. Financial Analysts typically have a strong background in finance and accounting. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop trading strategies and make investment recommendations. Quantitative Analysts typically have a strong background in mathematics, statistics, and computer science. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. They work with data scientists to develop and implement machine learning algorithms. Machine Learning Engineers typically have a strong background in computer science and machine learning. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Data Visualization Engineer

Data Visualization Engineers design and develop data visualizations that communicate data insights to users. They work with data analysts and data scientists to create clear and concise visualizations that can be easily understood by stakeholders. Data Visualization Engineers typically have a strong background in design and data visualization. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Database Administrator

Database Administrators manage and maintain databases. They work with database users to ensure that data is secure and accessible. Database Administrators typically have a strong background in computer science and database management. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Software Developer

Software Developers design and develop computer software. They work with users to understand their needs and develop software that meets those needs. Software Developers typically have a strong background in computer science and software development. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Web Developer

Web Developers design and develop websites. They work with users to understand their needs and develop websites that meet those needs. Web Developers typically have a strong background in computer science and web development. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Project Manager

Project Managers plan and execute projects. They work with stakeholders to define project goals and objectives, develop project plans, and manage project risks. Project Managers typically have a strong background in project management. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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Technical Writer

Technical Writers create and maintain technical documentation. They work with engineers and other technical professionals to translate complex technical information into clear and concise language that can be easily understood by users. Technical Writers typically have a strong background in writing and technical communication. This course can help you develop the skills you need to be successful in this role. It will give you an overview of the big data landscape and teach you how to work with big data tools.

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