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. Learners are eligible for the following discounts in Spring 2025:
- Current University of Minnesota affiliation, 90% discount
- Acad./non-profit affiliation, 60% discount
- For-profit affiliation, 30% discount
*HPC for Ag, and instructor-led refresher courses are free for all students.
Courses
Instructor-led courses have enrollment is capped at 30 learners per course, asynchronous courses have no enrollment cap. Our Spring 2025 courses are listed below.
Spring 2025 courses begin in January.
Asynchronous Courses
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.
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.
Instructor Led Course
HPC for Ag: Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources
If you are a researcher that works in the Agri-food domain (e.g., breeder, molecular biologist, food scientist, socioeconomist), you know a little bit of programming (e.g., in R and/or Python), but you feel a little limited (e.g., some of your calculations run for days on your laptop), then you could benefit from this course. We wish to show you how to step up to the next level, improve your coding efficiency, and make use of High Performance Computing (HPC) and Cloud resources readily available to you.
Familiarity at a beginner level with a programming language (e.g. Python, SQL, R, JavaScript, or Scala) is required. Given the nature of course material, some familiarity with the Python language is recommended.
Monday's March 17 - May 12, 2025, 10:30am - 12:00pm Central Time
Demystifying the UNIX command line
The UNIX command line is much more powerful than a point-and-click interface. It allows you to perform all of the same tasks plus many, many more. Most importantly, once you master the basic command line, you will learn in this course how to automate arbitrary sequences of commands.
Monday's February 3 - 17, 10:30am - 12:00pm Central Time
Working remotely and scheduling jobs at data centers
Sometimes the work we have to perform is just too large for the machine we are on and it is useful to be able to login remotely to more powerful supercomputers. For example, some tasks (e.g., genome assembly) require immense amounts of working memory (i.e., RAM), while others require very powerful processors or excessive storage. For these tasks, the Advanced computing centers across the nation has a variety of resources to tap, and these will be the topic of this course.
Monday's February 24 - March 3, 2025, 10:30am - 12:00pm Central Time