The Top 5 Must-have skills for Geospatial Data Science
Tags: GIS, Data Scientist, Geospatial, Education, Data Science

Programming Language

Python and R languages— Image by the author.

There is no doubt programming is an essential skill for the geospatial data scientist as well as GIS people. Today, even all GIS desktop packages have an integration with both Python and R languages. I won't go into detail about which one to learn — both are valuable.

The easiest way for GIS folks to start coding is either to use their familiar desktop GIS software — both QGIS and ArcGIS has Python and R integration. While this is a great way to start with less barrier, you also need to use your choice of programming language outside your desktop software.

If you pick Python, find your favourite Integrated Development Environment (IDE )— Popular IDE’s include Visual Studio Code and Pycharm. Also, it would be best if you learned Jupyter Notebook environment, which is one of the most widely used tools in data science.

For R users, similar tools exist like R Studio, which also have Markdown notebook functionalities. Other IDEs and Jupyter Notebooks are also supporting the R language.

SQL

Standard Query Language (SQL) — Image by the author.

As a GIS person, you have probably come across SQL. If you are an SQL Guru, good for you, but if you have gaps to cover, it is time to learn enough SQL, especially spatial SQL. While R and Python are sort of new, SQL is old and used everywhere in companies, which makes it a highly demanding skill in many jobs.

For geospatial people, you could greatly benefit learning SQL and relational databases. Again, you can start your learning journey with your favourite GIS desktop software.

PostGIS is probably the most widely used spatial relational database. It is a sort of de facto spatial SQL adopted by many, including Google’s BigQuery GIS. However, SpatiaLite is a lightweight spatial SQL and easy to start. T

Version Control

Version Control with Github — Image by the author.

Writing Code is messy!. You probably have many files with the same name with some number suffix. Version control takes away the burden of having the same multiple files and tracking each one. It is a way to make changes on a single centralized file or code. This is helpful personally when working with your own projects but also super helpful (mandatory) for collaborating with others.

As Geospatial data science becomes more collaborative, some development operations like Git and Git Versioning is an essential skill and a requirement for many job vacancies.

Learning Git or versioning might seem daunting while learning, but as soon as you use it daily, this will become second nature. If you are not comfortable with terminals yet, most IDEs have has integrated source control and includes GIT support, which can help you reduce the burden.

Cloud

Cloud — Image by the author.

The future of data science and specially Geospatial data science is on the cloud. The next-generation geospatial data scientist, therefore, need to have a working knowledge of cloud computing. Not only Big datasets makes the use of cloud computing essential, but also the scalability and immediacy of spatial data analytics.

Today, with either a free trial, free sandboxes or open-source tools, you have the ability to learn and practice how to run big data geospatial processes using the cloud.

The three most used Cloud platforms include Google Cloud Platform(GCP), Microsoft Azure and Amazon cloud (AWS). The choice can be daunting, but any skills with one platform can easily be transferred to another one.

AI and ML

Artificial Intelligence and Machine Learning — Image by the author.

Artificial Intelligence(AI) and Machine Learning(ML), especially deep learning, are disrupting the GIS Industry. The abundance of both satellite images as well as unstructured text with location component makes the application of AI and ML appealing in GIS.

As a Geospatial data scientist, the future is promising as we have only scratched the surface of computer vision and Natural language application with geospatial data.

Luckily today, we have both low code and no code ML and AI tools for a geospatial data scientist, that lowers the barrier of entering this exciting new field. With Fastai, you can also create a state of the art deep learning for Geospatial applications using both computer vision and Natural language processing and transfer learning.

Bonus — Soft Skills

Do not also forget to work on your soft skills as these can differentiate you from the rest of Geospatial data scientists.

Communication skills: You have already seen the importance of communication skills in your GIS career. In the data science world, this becomes much more important as this new field requires the collaboration of many partners inside and outside your organisation.

You have already seen the importance of communication skills in your GIS career. In the data science world, this becomes much more important as this new field requires the collaboration of many partners inside and outside your organisation. StoryTelling: Data visualizations and storytelling still plays an important role in the Geospatial data science role.

Data visualizations and storytelling still plays an important role in the Geospatial data science role. Geospatial analytics acumen: Bring your geospatial business acumen to solve novel and old problems with Geospatial data science.

Bring your geospatial business acumen to solve novel and old problems with Geospatial data science. Ethical skills (privacy): Location privacy and ethical considerations will be an integral part of your geospatial data science world as you will deal with big data.

Final thoughts

The future of Geospatial data scientist is bright. The skillsets for the GIS role has evolved and shifted over the past 10 years. In this article, I have shared with the essential skills necessary to thrive as a Geospatial data scientist.

Learn a programming language, and SQL to level up your analytical knowledge and automate geospatial workflows. Github and version control has become a must-have skill in the past few years, so do your self a favour and learn it.

Furthermore, the cloud is enabling today many geospatial data science applications and therefore, has become a necessary skill set. Finally, AI and ML have a tremendous prospective application for the Geosatpial world, so it is wise to learn them.

I hope the best for your journey and transition from GIS to Geospatial data science. Ask questions here and connect with on LinkedIn to discuss further Geospatial data science education and skills.