Explore the CGA's 2026 Training Schedule
The Center for Geographic Analysis (CGA) is re-vamping its training workshop offerings for 2026! Free for Harvard ID holders, the workshops will teach various GIS and spatial concepts through applying the technologies ArcGIS, QGIS, Python, R, KNIME, Google Earth Engine and more.
Below is a short overview of upcoming CGA workshops. View all of the workshop dates and details, and sign up at the CGA's teaching page.
GIS Institute
A two-week training institute held in January and May every semester. Research proposal required.
Introduction to GIS (1/30/2026)
Introduces what Geographic Information Systems (GIS) are and what they are useful for. Students will use the desktop GIS software QGIS, and the web GIS software ArcGIS Online to perform various GIS tasks.
Geocoding with ArcGIS (2/6/2026)
Introduces the concept of geocoding and how different geocoded datasets can be used. Students will geocode single addresses and cities using Bing Maps, Google Maps, and ArcGIS Online.
Visual Programming for GIS using KNIME (2/13/2026)
Introduces no-code visual programming for geographic analysis using KNIME. Students will use the KNIME Analytics Platform and provided example workflows to perform various GIS tasks.
Getting Started Making Maps with QGIS and ArcGIS (2/20/2026)
Learn how to make maps using the two primary desktop GIS software programs QGIS and ArcGIS Pro. Become familiar with the fundamental principles of cartography, and learn some map design basics.
Getting Started with Spatial Analysis in R (3/6/2026)
Introduces the concept of spatial analysis and what it can be used for. Students will use the R programming language to perform proximity, overlay, and geostatistical operations on a variety of data.
Satellite Imagery Analysis with Google Earth Engine (4/3/2026)
Introduces satellite imagery analysis with Google Earth Engine (GEE) using KNIME’s GEE extension. Students will use the KNIME Analytics Platform with the GEE extension and sample workflows to perform hands-on tasks.
Getting Started with AI for Spatial Data (4/10/2026)
Introduces participants to the practical use of Large Language Models (LLMs) for text classification tasks using real-world geotagged Twitter data. Participants will learn how to leverage Python and the LM studio to interact with advanced AI models such as Gemini, gaining foundational experience in calling Open AI APIs, designing effective prompts, and integrating LLMs into data workflows.
Python for Spatial Data and Data Science (4/7/2026)
Learn how to analyze large data sets using the Python programming language. Students will work with sample datasets and their own data sets for analysis.
