Summary
In this chapter, we defined the differences and commonalities between GIS, data science, and geospatial data science. As we discussed data science, we took a deep dive into the four pillars of data science, which include mathematics, computer science, domain and industry knowledge, and soft skills.
We also briefly discussed the stages involved in the data science process. Parts 2 and 3 of this book will provide you with more hands-on experience in implementing the data science process through exploratory data analysis, hypothesis testing, and in-depth data science use cases, covering a variety of topics and algorithms.
We also discussed how the principles of geospatial data science can be applied more broadly within the domain of spatial data science to solve problems at a smaller, microscopic level, as well as larger, astronomical scales. The power of geospatial data science is only starting to be realized as industries, data storage, and computing methodologies evolve. We’re excited that you’ve decided to embark on this learning journey with us and are even more excited to see what you achieve in your journey to become a geospatial data scientist.
In the next chapter, we’ll dive deeper into the world of geospatial data, which we briefly described in this chapter as being a representation of the real world in vector or raster format. We’ll also spend time in the next chapter discussing the rich sources of open geospatial data.
