Data Analysis

The aim of the study subject is to provide students with knowledge about data collected in agriculture, their properties, data collection methods, databases, their types, design, management systems and tools, big data and their storage and use. Students will gain knowledge of data analysis tools, connections between different agricultural databases, will be introduced to the concepts of smart farm based on digital technologies.

The aim of the study subject is to provide students with knowledge about data collected in agriculture, their properties, data collection methods, databases, their types, design, management systems and tools, big data, their storage and use.

The student must have listened to the following subjects: Mathematics, Physics, Information Technologies, Basics of Statistics, Computer Aided Design, Information Technology, Transport and Power Machinery, Crop Harvesting Machines.

1. General knowledge of data collected in agriculture. General data properties 2. Information systems for agricultural sectors (crop, livestock, etc.) 3. Data collection methods 4. Databases, their types 5. Database design 6. Database management systems and tools 7. Filling the database with data 8. Basics of query language SQL 9. Big data 10. Data storages 11. Data import/export between different formats. 12. Use of programs for data formation, transformation, analysis and export. 13. Application of machine learning and deep machine learning in agriculture 14. Analysis of digital data relationships between soil and meteorological conditions 15. Geographic database management systems 16. Intelligent crop production (data interaction between agricultural machinery-office-warehouse-garage, etc.). 17. Data optimization and practical use

1. Knowledge of database structure, ability to create and modify them; 2. Knowledge of basic SQL queries, ability to use them; 3. Knowledge of different data formats, ability to convert data between them; 4. Understanding of big data, ability to understand their usefulness and applicability in various fields; 5. Understanding of machine learning and deep machine learning, ability to understand their benefits in agriculture; 6. Quality of laboratory work and exercises and preparation and presentation of reports; 7. Correspondence of the course project to the formulated task and completeness of performance.