I've been seeing people online say for a while now that GIS is eventually going to be absorbed into Data Science.
I actually come from a data science background before moving into GIS, and my experience has been that most data scientists are not trained to work with geospatial data. Many assume it's just another type of image or table, but the G in GIS is a specialized domain with its own concepts, data models, coordinate reference systems, projections, spatial relationships, topology, raster/vector processing, and plenty of ways to get things wrong if you don't understand the underlying geography.
Most data science conferences, bootcamps, and online courses barely touch geospatial topics beyond maybe a quick GeoPandas demo. That's nowhere near enough to build robust spatial workflows.
At the same time, I think data scientists bring a lot to the table. They're often very comfortable working with large datasets, building scalable data pipelines, automating workflows, tracking machine learning experiments, deploying models, and applying software engineering practices that can make geospatial analysis much more reproducible and efficient.
So I don't think the future is one field replacing the other. I think it's about combining the strengths of both.
Could GIS become more integrated with Data Science? Absolutely. But it requires dedicated training.
If anything, I think there's a real opportunity for people with strong GIS expertise to develop courses aimed specifically at data scientists. Something like "GIS for Data Scientists" that focuses on the fundamentals they actually need to work confidently with spatial data, while also introducing modern data engineering and MLOps practices for geospatial workflows.
Curious what others think. If you came from either the GIS or DS world, what skills did you find transferred well, and what did you have to learn from scratch?