GEMS Learning - Getting Started Using Data to Support Decisions
GEMS Learning provides modular non-credit digital and data science education for working professionals and students in food, agriculture, and natural resource application areas. Across the curriculum, instructors have built their course content from their own work executing large-scale data science projects to solve agricultural problems.
Series: Digital Agriculture
Data is everywhere in agriculture, but knowing what to do with it isn't always easy or straightforward. These modules will give you the basic tools for analyzing a decision-making context, evaluating the data needs, collecting or integrating data, and then performing basic analysis and visualizations.
Getting Started Using Data to Support Decisions
This course is intended for students with limited or no background with data, but have practical experience working in agriculture, food, or environment.
Data is everywhere in agriculture, but knowing what to do with it isn't always easy or straightforward. This module will give you the basic tools for analyzing a decision-making context, evaluating the data needs, collecting or integrating data, and then performing basic analysis and visualization in Excel, R, Python, and QGIS. We will also cover some of the common pitfalls of using data to drive decision-making to be sure you and your teams can avoid them.
Fees:
- Fee: $280 (no fee for U of M affiliated)
- Scholarships are available (see the link on the registration page). See the full line-up of courses and register.
The other course in the Digital Agriculture series is:
- Using Simple Models to Guide Decision Making
Date, time and location:
- Mar. 19, 2024
- 10:00am to 12:30pm
- Online
Analyze a decision-making context to identify what the decision is, what options exist for decision-making, who the decision-maker(s) is (are), what mechanisms of decision-making are used
Identify what data exists and what data needs to be collected or integrated to support decision-making
Perform basic data visualization in Excel, R, Python, and QGIS including importing data (csv, shapefiles), subsetting data, and making boxplots, scatterplots, histograms, choropleth maps, and dot maps
Describe the decision-maker(s) each data visualization is targeted at and the appropriate uses and pitfalls of interpretation to guard against
- Fee: $280 (no fee for U of M affiliated)
Scholarships are available (see the link on the registration page). See the full line-up of courses and register.
The other course in the Digital Agriculture series is:
Using Simple Models to Guide Decision Making