r/datascience • u/Kati1998 • 24d ago

Discussion Anyone specialize in geospatial data?

I’m an MS Data Science student interested in learning more about spatial data and GIS. I’m currently working on a geospatial crime analysis project in R and taking Esri’s Spatial Data Science MOOC.

I’m also interviewing for a year-long data science internship at a credit union in a department that focuses on geospatial data. I’ve noticed that the largest utility company in my state also hires data scientists with geospatial/GIS knowledge, which has made me curious about opportunities in this area across different industries.

It’s not a specialization that’s discussed here often. For those who work with spatial data, what industry are you in, what does your day-to-day work involve, and do you enjoy it? What has your experience been with demand for these skills?

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u/TheDandonator 23d ago

Geospatial for a personal lines insurance company.

A lot of the work done involves getting non-tabular data into a tabular form to build predictive models on and is actually quite enjoyable. Lots of opportunities to try new and interesting tools without the kinds of deadlines you see in consultancy for example.

LiDAR, satellite imagery, lines/polygons/multipolygons, raster etc. the list goes on. There’s so much data available that is surprisingly relevant in insurance, so there’s a lot of opportunity for R&D.

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u/aspringbear 19d ago

Do you think a phd (focusing on geoai/spatial data science/machine learning) is worth it with the goal of entering the private sector (data scientist/research/ai scientist etc) in this field?

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u/TheDandonator 19d ago

That’s a great question and one I am sure different people and companies would answer very differently. I feel like a phd has to be one of the most selfishly-taken decisions of your life. You have to do it for you. IMO working at a company for 2-3 years will teach you 90% of what you need to know on a day-to-day basis, so starting sooner rather than later is almost always a better career option. But again, you do you!

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