Transitioning from GIS Analyst to Data Scientist: Key Insights
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Chapter 1: The Shift in GIS Roles
Recently, ESRI (Environmental Systems Research Institute) announced the discontinuation of ArcMap, with support ending on March 1, 2026. They are advocating for users to shift to ArcGIS-Pro, which is designed for data science applications, including machine learning and analytics.
This shift comes as no surprise; the geospatial field has been increasingly incorporating elements of data science, deep learning, and artificial intelligence over the past five years. As a result, the role of a GIS analyst is set to evolve into that of a data engineer in the near future, marking a significant disruption in the industry.
Many sectors are experiencing transformation due to the data revolution. You may have seen reports about companies downsizing departments and outsourcing tasks to improve cost efficiency. This trend is expected to become more prevalent.
Section 1.1: Skills in Demand
If you've explored job postings for geospatial positions, you will likely notice that familiarity with programming languages such as R, Power BI, SQL, Java, AWS, or Python is often required. Knowledge of various machine learning algorithms like random forests, K-nearest neighbors, and decision trees is also becoming commonplace.
Additionally, employers are seeking expertise in relational databases such as PostgreSQL, MySQL, and PostGIS. If math and statistics were not your strong suits in high school, you may find the transition challenging.
In the past, being a GIS analyst primarily required proficiency in analytical tools like ArcMap and QGIS, along with some cartographic abilities. What does this mean for those analysts who lack programming skills or a foundation in data science?
Subsection 1.1.1: Addressing Redundancy
As the landscape changes, it’s crucial to adapt to remain relevant. Here are eight strategies to avoid obsolescence:
- Reassess Educational Background: Geospatial degrees may lose significance as fields like computer science, statistics, and software engineering become more relevant for GIS data scientist roles.
- Embrace Software Evolution: Software like ArcMap may become outdated. Companies that fail to integrate analytics into their operations may follow suit. Open-source languages like Python, Java, and R are likely to gain prominence.
- Expand Networking Efforts: Building a network is vital. Connect with other professionals transitioning to data science through conferences, LinkedIn groups, and local meet-ups.
- Gain Open-Source Knowledge: Familiarity with open-source software will be increasingly valuable. Tools like Python, R, and Java are widely used in the data science community.
- Prioritize On-the-Job Learning: Companies need to recognize that developing these skills takes time and be open to training programs. Clearly define your learning objectives as time is of the essence.
- Identify Your Niche: Data science encompasses a wide range of topics. While mastering everything is ideal, focus on your interests and start there.
- Self-Directed Learning: Utilize online resources such as Udemy, Coursera, and tech boot camps. Platforms like Stack Overflow and Free Code Camp can also be invaluable.
- Be Patient with Yourself: Progress takes time. Understand that this is a journey, and you will encounter challenges along the way. Set specific goals and timelines to help guide your transition.
In summary, make this transition part of your long-term goals. Be proactive, as the shift is already underway. Preparing early will position you better for the inevitable changes in the industry.
Chapter 2: The Harsh Reality of GIS Careers
The first video, "The Harsh Reality of Being a GIS Analyst," explores the challenges and realities faced by professionals in this field. It highlights the critical need for adaptation and skill enhancement in the evolving job market.
Chapter 3: Reasons to Rethink GIS Careers
The second video, "5 Reasons You DON'T Want a Career as a GIS Analyst," delves into the drawbacks of this profession and emphasizes the importance of staying informed about industry trends to make informed career choices.