Home GIS GIS Analyst To GIS Data Scientist – 8 Things You Should Know

GIS Analyst To GIS Data Scientist – 8 Things You Should Know

ESRI (Environmental Service Research Institute) recently announced that they will no longer be releasing the latest version of ArcMap and that it will not be supported after March 1, 2026. They are encouraging users to transition to their latest product, ArcGIS-Pro, which is best suited for a data science machine, learning, deep learning and analytics.

It does not come as a surprise, as for the last five years, the geospatial industry has really grown and slowly incorporated aspects of data science, deep learning, and artificial intelligence. 

This means that in the next 5 years, a GIS analyst will be a data engineer. In other words, disruption is here!

Many jobs are becoming disrupted by the data revolution. You have probably heard in the news about company X letting go of a whole department as they are outsourcing it to save on cost and efficiency. Most of the time, they assign the tasks to a start-up. 

This is going to be a common occurrence in the near future.

If you have ever looked at a job advertisement regarding any geospatial position, one must know or be familiar with R programming, Power BI, SQL, Java, AWS, or Python. Some even require you to understand different types of machine learning algorithms such as random forest, K-nearest, and decision tree.

 Others expect you to be an expert in rational databases such as PostgreSQL, MySQL, and PostGIS.Basically, if in high school you were not a fan of math and statistics like me, you should be in for a rude awakening.

In the past, all that was required to be a gis analyst was knowledge of some analytical tools such as ArcMap, QGIS and some cartographic skills. What does this mean for a GIS analyst who has no knowledge of programming, let alone data science?

Sticky notes on a glass wall

8 (eight) ways you can avoid becoming redundant

  1. Geospatial-related degrees may become irrelevant. A degree in computer science, statistics, software engineering, informatics, and information management will be able to fill the roles of a GIS data scientist, hence having a degree in GIS may not be required. However, this also depends on the company and what it intends to achieve.
  2. Many GIS software will become irrelevant. ESRI has mentioned that they will discontinue the production of ArcMap in the near future. I foresee other software doing the same if they do not incorporate analytics into their workflow. Python, Java, R studios, and other open-source software will take their place, as they have become very popular in the geospatial community.
  3. Networking will be a must. You need to broaden your network if you want to make this transition. This means one needs to find fellow geospatial practitioners who are or are in the process of becoming geospatial data scientists. They can be found at conferences, LinkedIn groups, events, regular meet-ups, e.t.c.
  4. Knowledge of open-source software will be in demand. Data scientists are renowned for their use of open-source software. This will cause a spike in the demand for individuals who can utilise OSS as they are free. This software includes python, R studios, and Java.
  5. Learning on the Job will be a must. Companies will have to understand that these skills take time to build, hence job training is something they must be open to. However, one needs to be clear on their goal, as time is of the essence.
  6. Find your sweet spot. Data science is a broad topic that includes machine learning, deep learning, artificial intelligence, software development, full-stack, R&D, data analysis, and visualization. Ideally, if you manage to master all these skills, that would be great, but if you are starting out, find what interests you, and then start from there.
  7. Most skills will be self-taught. You will have to make use of online tutorials such as Udemy (which I highly recommend), Coursera, hackathons, tech boot camps Youtube, and Ed X. Other online knowledge-sharing platforms such as Stack-Over Flow, Free code camp, and OSOA (Open Source Question and Answer) will also come in handy.
  8. Give yourself time. Rome was not built in a day. This is an ongoing process and it is a destination, not a one-day event. You will experience a lot of hurdles along the way, but do not give up.

In a nutshell, make it part of your resolutions i.e., have a specific timeline and stick to it. As stated earlier, be clear on your goals and do everything to achieve them. Whether you like it or not, this transition is happening. One needs to prepare themselves accordingly as many people will be affected by this change, so you better start the transition early!

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