During and shortly after my undergraduate education, I tried a few different career paths that didn’t work out. Eventually, I felt like GIS could be a way to continue using my environmental degree while pursuing a renewed interest in tech. This was very much true- with some caveats. To be successful in GIS, you should be able to code and have some knowledge in statistics. During my graduate education, I took as many computer science and programming-centric courses I could- and I still felt like there was much, much more to learn. But, it was enough to get me through the door.
I am creating four non-distinct, overlapping areas that I think anyone interested in GIS, spatial data science, and engineering should study- because that is what I did. They are:
Computer Science
Computer science is a broad field, and can be used in a large number of disciplines. It is the study of computers and computational systems. It has many components, but my own experience tells me that its concepts in data structures and algorithms are most fundamental and important. Knowledge of computer science can be extremely beneficial for working with data efficiently.
Software Engineering
Software engineering is the systematic design, development, testing, and maintenance of software. It is not to be forgotten that software engineering also encompasses project management and quality assurance. It goes well beyond ad hoc scripting- but if you can learn to code, you can learn to be a software engineer. It just takes dedication, like anything else. Knowledge of software engineering can help bring geospatial data and information solutions to your company, the government, and the masses.
Data Engineering
Data engineering is just a niche within software engineering, focused on data collection and processing. Data engineers build data pipelines- that is, software that helps move data from one place to another. Data pipelines should be built in a reproducible, versioned, automated, scalable, and robust way- minimizing the potential for data loss or data duplication; systems do fail and bugs are inevitably introduced in the software development process, so engineers attempt to mitigate this as much as possible. Usually, data engineers work to consolidate data from a wide variety of sources into a single, standardized data warehouse. The data that is collected is made available to data analysts and scientists for use in creating models, visualizations, dashbaords, and reports. Data engineering is my discipline at the time of writing this post.
Spatial Data Science, GIS, and GIScience
Data science is a multidisciplinary field that combines statistics, artificial intelligence and machine learning, computer science, and industry or scientific knowledge to gain insights from data and predict future outcomes. There can be some overlaps with data engineering when it comes to data collection and munging, but data science specializes in the creation of models to make predictions based on historical data. Often times, the models data scientists create are used as integral parts of software offerings. Other times, the findings are purely academic or ad hoc in nature.
Spatial data science is all of this, but focuses on data with a geographic component, which is an added layer of complexity. Spatial data science is very similar to geographic information science (GIS or GIScience), which is the basis for (and is also semantically similar to) geographic information systems (also GIS). While geographic information systems are the tools primarily concerned with the management, visualization and analysis of geographic data, geographic information science is the theory underpinning geographic information systems. Spatial data science, meanwhile, is more practical in nature than geographic information science by attempting to answer real world questions and often places an emphasis on predictive analyses.
TLDR: you can perform spatial data science using geographic information systems, but you cannot perform spatial data science without knowledge of geographic information science.