GEMS Learning - Using Simple Models to Guide Decision Making
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.
Using Simple Models to Guide Decision Making
What happens when there’s so much data, you and your team don’t know where to look to use the data to support decision-making? This happens all the time in agriculture. In this course, learners will see how simple modeling techniques can be used to prioritize decision options and focus the attention of decision-makers on the information that matters.
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:
Date, time and location:
- Apr. 9, 2024
- 10:00 am to 12:30 pm
- Online
Analyze a decision-making context to establish when data are too complex to be used directly to support decisions
Decompose datasets into standard criteria useful for decision-making
Develop weighting schemes for multiple sources of data using individual decision-maker and group-based approaches
Build simple and correct linear and geometric weighted models to generate decision-support indices
Use decision-support indices iteratively in a group setting to understand the benefits and costs of different decision options
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: