Data Science in Agriculture: Python Tools for Farm Analytics
Learn to analyze agricultural data using Python libraries like pandas and seaborn. Create interactive maps and forecast trends in just one hour.
Learn to analyze agricultural data using Python libraries like pandas and seaborn. Create interactive maps and forecast trends in just one hour.
This hands-on guided project introduces you to the application of data science in agriculture. In just one hour, you'll learn how to use essential Python tools for statistical analysis of agricultural data and create interactive visualizations. The course focuses on practical skills using popular libraries such as pandas for data manipulation and seaborn for data visualization. You'll work with real agricultural datasets, learning how to download, prepare, analyze, and visualize data. Key skills covered include reading CSV files, converting data to DataFrames, preprocessing, performing statistical analysis, and creating various visualizations including trend lines for forecasting. The project culminates in building interactive maps to display data changes over time, a crucial skill for modern agricultural analysis. This course is ideal for beginners with some knowledge of Python and statistics, providing job-ready skills applicable in the rapidly evolving field of agricultural data science.
Instructors:
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What you'll learn
Read and process CSV files containing agricultural data
Convert raw data into pandas DataFrames for efficient analysis
Preprocess agricultural datasets to ensure data quality and consistency
Perform statistical analysis on agricultural data and interpret summary statistics
Create informative visualizations using pandas and seaborn to represent agricultural trends
Build interactive maps with Plotly to display geographical agricultural data over time
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This guided project focuses on applying data science techniques to agricultural data using Python. Participants will learn to handle agricultural datasets using popular Python libraries such as pandas and seaborn. The course covers essential skills for data analysis in agriculture, including reading and preprocessing CSV files, performing statistical analysis, and creating various visualizations. A key component is learning to build trend lines for forecasting future trends, a crucial skill in agricultural planning. The project also introduces interactive mapping techniques using Plotly, allowing learners to visualize data changes over time. This hands-on approach provides practical experience in using data-driven methods to inform agricultural decision-making, covering soil, water, and economic data analysis. The course is designed to equip learners with job-ready skills that are increasingly valuable in modern farming and agricultural management.
Fee Structure
Instructor
2 Courses
Pioneering AI Professor and Expert in Complex Systems Modeling
Dr. Yaroslav Vyklyuk has established himself as a distinguished academic leader serving as a full professor at the Lviv Polytechnic National University's Department of Artificial Intelligence Systems. His prolific academic career is marked by authoring over 210 scientific works and 10 monographs and books, while actively serving on the Editorial Board of six international scientific journals. As a member of the Academic Councils for Ph.D. and DrSc thesis defense in Mathematical modeling and computational methods, he plays a crucial role in shaping future researchers. His extensive research portfolio encompasses Data Science, Applied System Analysis, and Mathematical Modeling, with particular emphasis on decision-making in complex dynamic systems including socio-economic, geographical, tourist, and crisis systems. Dr. Vyklyuk's innovative approach combines cutting-edge technologies such as Artificial Intelligence, Data Mining, Big Data, and Parallel Calculations with traditional methodologies like Statistics, Econometrics, and Econophysics, implementing these advanced mathematical methods into practical applications across information systems, web platforms, and geographic information systems
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