2026
Big Data e Análise de Dados Agrícolas
Name: Big Data e Análise de Dados Agrícolas
Code: FIT16247O
3 ECTS
Duration: 15 weeks/78 hours
Scientific Area:
Ciências de Dados
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
The primary objective of the Big Data and Data Analysis in Agriculture course is to provide students with the essential knowledge and skills required to leverage big data techniques and analytical tools for agriculture. This includes understanding how big data can inform decision-making, improve agricultural practices, and enable data-driven strategies for sustainable agribusiness operations. The course also covers data processing techniques, data visualization, and the application of advanced analytics to optimize agricultural outcomes.
Contents
U1: Data Collection & Integration
? Identify and gather data from various agricultural sources (e.g., IoT devices, open datasets).
? Learn the techniques to integrate and manage large datasets.
U2: Data Engineering & Storage
? Explore the principles of data engineering and storage solutions in big data, including cloud and distributed
databases.
? Implement ETL (Extract, Transform, Load) processes for agricultural data management.
U3: Data Analytics & Visualization
? Use statistical and machine learning tools to analyze agricultural data.
? Create visualizations and dashboards for decision-making in agriculture
? Identify and gather data from various agricultural sources (e.g., IoT devices, open datasets).
? Learn the techniques to integrate and manage large datasets.
U2: Data Engineering & Storage
? Explore the principles of data engineering and storage solutions in big data, including cloud and distributed
databases.
? Implement ETL (Extract, Transform, Load) processes for agricultural data management.
U3: Data Analytics & Visualization
? Use statistical and machine learning tools to analyze agricultural data.
? Create visualizations and dashboards for decision-making in agriculture
Teaching Methods
The course is based on a mix of theoretical lectures and practical lectures and tutorials. The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples. The main objective of the practical classes is to familiarize students with the software to perform the analysis and data explorations task.
Assessment
Evaluation variables:
a)Project*
b)Final Exam
Grading will result from the following evaluation variables weights:
a) 50%
b) 50%
To pass a minimum of 9,5 must be obtained in the final exam.
*Groups composition defined in the Labs
a)Project*
b)Final Exam
Grading will result from the following evaluation variables weights:
a) 50%
b) 50%
To pass a minimum of 9,5 must be obtained in the final exam.
*Groups composition defined in the Labs
Accreditations and Certifications
