Open-Source Python for Spatial Statistics and Machine Learning
Blueprint for impact
Get a look at the pillars shaping this course.
While development is still underway, these highlights offer an early look at the skills and outcomes you’ll be working toward:
Master spatial data science concepts
Dive into advanced spatial statistics and machine learning to uncover patterns, predict trends, and analyze suitability using open-source Python libraries.
Build with open-source tools
Learn how to use GeoPandas and related libraries to write, adapt, and optimize spatial data science workflows that are flexible and cost-effective.
Develop reusable code workflows
Practice coding in Python to create efficient, modular scripts that support scalable spatial analysis—ideal for professionals working with large datasets.
Visualize and communicate findings
Create interactive, insightful geographic visualizations using Jupyter Notebooks, helping translate complex analysis into clear decision-making tools.
"Spatial data science is evolving rapidly, and professionals need access to free and open source (FOSS), flexible, scalable tools that keep up. This course is being developed to give learners the skills to use open-source Python tools to explore spatial patterns, predict outcomes, and build practical, reusable workflows. We are designing it to meet the growing demand for geospatial analysis that is both powerful and accessible. I am especially excited about how this course will open doors for learners to work across sectors using tools that are future-ready and cost-effective."
Flexible. Applied. Built for business.
Exploring this for your team, not just yourself? You’re in the right place. This course is still in development, making it an ideal time to explore how it could support your workforce goals. Whether you're planning ahead or seeking a strategic fit, we’ll help tailor the experience so it delivers value for your team from day one.
Open-Source Python for Spatial Statistics and Machine Learning
Advance your spatial data science skills with Python and open-source tools. Learn to apply spatial statistics and machine learning to solve real-world problems with flexible, future-proof workflows.
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Course description
Course outcomes
The learning experience
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This course is designed by RIT and delivered on the edX platform. Enrollment is available exclusively at edx.org. Please visit the edX website to explore more detailed information about pricing options, start dates, time commitment and more.
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Hear from the experts
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Insight
"Spatial data science is evolving rapidly, and professionals need access to free and open source (FOSS), flexible, scalable tools that keep up. This course is being developed to give learners the skills to use open-source Python tools to explore spatial patterns, predict outcomes, and build practical, reusable workflows. We are designing it to meet the growing demand for geospatial analysis that is both powerful and accessible. I am especially excited about how this course will open doors for learners to work across sectors using tools that are future-ready and cost-effective."
Value
Flexible. Applied. Built for business.
Exploring this for your team, not just yourself? You're in the right place. RIT Certified courses and certificates deliver high-impact ROI whether used off the shelf or tailored with our team to align with your workforce strategy. Each offering builds in-demand skills that help teams grow, retain talent, and stay ahead in a world of constant change. We don’t just deliver training. We help you plan for what’s next.
Pathways and certificates
This course is part of a broader mastery certificate.
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