GEMS Learning - Introduction to Spatial Data Analysis in Python
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: 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. Through this 3-week introductory course, you will 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. In each 2.5 hour lecture, you will have the opportunity to immediately practice your new skills via hands-on exercises focused on agri-food applications.
GEMS Learning – Introduction to Spatial Data Analysis in Python
Week 1: Introduction to spatial data and mapping in Python
Week 2: Basic geocomputation with vector data in Python
Week 3: Basic geocomputation with raster data in Python
The course will be delivered via a Jupyter Notebook hosted on the GEMS Informatics Platform. You do not need to have R or RStudio installed on your machine to participate.
Fees
- Fee: $525 (no fee for U of M affiliated)
- This is a three-part course that will take place at the same times on dates between February 5 and February 26.
- Scholarships are available (see the link on the registration page). See the full line-up of courses and register.
The other course in the Accounting for Location in Agriculture in Python series is:
Date, time and location:
- Feb. 5, 2024 to Feb. 26, 2024
- 10:00am to 12:30pm
- Online
Week 1:
- Introduction to spatial data and mapping in Python
- Introduction to the GEMS platform and Jupyter Notebook
- Describe why spatial?
- Importing point, polygon & raster data
- Creating basic maps
- Layering features in maps
Week 2:
- Basic geocomputation with vector data in Python
- Introduction to vector data
- Attribute data operations
- Spatial data operations
- Geometry operations
Week 3:
- Basic geocomputation with raster data in Python
- Raster data in Python
- Raster manipulation
- Spatial operations
- Geometry operations
- Raster-Vector interactions
- Fee: $525 (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.
Other courses in the Accounting for Location in Agriculture in Python Series: