GEMS Learning
COURSE DETAILS AND REGISTRATION
AGRI-FOOD DIGITAL AND DATA SCIENCE TRAINING
GEMS Learning currently provides modular, non-credit digital and data science training for working professionals and students interested in hands-on food, agriculture, and natural resource applications. Across the curriculum, instructors have built their course content from their own work tackling small- to large-scale, often complex, data science projects to solve real-world agricultural problems. Our courses give you the practical knowledge to tackle data-science challenges across the agri-food sciences.
Instructor led online courses means getting your questions answered in real time. Courses range from a single two-hour course to modules with multiple courses that occur over several weeks.
Course Fees
Course fees are based on the number of contact hours in each course.
Current University of Minnesota (UMN) staff, faculty and students can enroll for free. Make sure to enroll using your umn email account, and tick the “UMN affiliated” button at checkout.
Non-UMN learners are eligible for the following discounts in Fall 2023 and Spring 2024:
- Acad./non-profit 1-3 courses, 60% discount
- Acad./non-profit 4-plus courses, 80% discount
- For-profit 1-3 courses, 30% discount
- For-profit 4-plus courses, 50% discount
You can enroll at any time to receive the 1-3 course discount. To obtain the four-plus course discount all your chosen courses must be in the same basket at check out for the discount to be applied. Retroactive discounts for four or more courses cannot be given.
Courses
Enrollment is capped at 30 learners per course. Our Fall 2023 and Spring 2024 courses are listed below.
Fall 2023 courses start October 2nd, register today.
Computing Basics for the Agri-food Sector
Are you a field or bench scientist and always wanted to feel more comfortable with your computing skills? These courses are designed for those who have never used the command line, but realize that the responsibilities they have or will soon take on require them to automate tasks. It will teach basic UNIX command-line skills, enable participants to work remotely on more powerful machines, create and run scripts to automate complex workflows, and synchronize your scripts with the larger community with Github.
Introduction to Data Analysis with R
Would you like to use R for your data analysis needs? This two hour introductory workshop is designed for those who are new to R and are interested in learning the basics of using R for data analysis. You will learn how to use R on the GEMS platform, starting from installing packages, setting up your workspace, to the basics of R object types.
Accounting for Location in Agriculture in R
Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? Is accounting for spatial dependency in your analyses critical to your work? Or do you need to create a continuous surface of data (i.e., raster) based on a sample point date taken at selected locations? Learn how to work with spatial data in R, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data.
Each module includes the opportunity to practice your new skills via hands-on exercises focused on agri-food applications.
Accounting for Location in Agriculture in Python
Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? This course is designed for those who are interested in explicitly accounting for location in their analyses. Learn how to work with spatial data in Python, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data. Each module includes the opportunity to practice your new skills via hands-on exercises focused on agri-food applications.
Spatio-Temporal Accounting of Biotic Threats
Has your research, studies or work required you to examine the geographic distribution of various biological species? This could be for crop protection, forestry, environmental protection, environmental impact assessment, urban and natural landscape design and development, or simply an interest in any given species, whether insects, pathogens or weeds? Then you are at the right place; species distribution models allow us to understand the potential and realized distribution of various species across our landscapes at different scales.
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.