Ideal for beginners with some knowledge of Python and statistics, in this one-hour hands-on guided project, you will learn how to use essential Python tools for statistical analysis of agricultural data and present that data on interactive maps.
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Data science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.
This hands-on guided project will prepare you to handle agricultural datasets using these Python tools. You will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.
You will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.
What you'll learn
After completing this project, you will be able to:
- Read a CSV file
- Convert the CSV file to a DataFrame
- Preprocess the data
- Perform statistical analysis of the data and display various summary statistics
- Visualize data using pandas and seaborn
- Build interactive maps using Plotly
