Spatial data science is an emerging field in the data science space that includes geographic and spatial data to allow data scientists to create deeper insights from the data they collect. Knowledge of geographic information systems (GIS) is an in-demand skill in the data analysis and mathematics field, with 10% of job postings including GIS as a required or desired skill for applicants to possess. Through the courses in this mastery certificate, you’ll gain skills and knowledge of geospatial technology, spatial data science application development, Python, and more to prepare you to advance your career in geospatial, data science, or other industries involving geospatial data.
Is This Mastery Certificate For You?
This mastery certificate is designed for data science professionals currently working in geospatial analysis, data analysis, data science, or other types of computerized data looking to advance their skills. There are two pathways you can choose from in this mastery certificate depending on your professional background, existing knowledge, and what you want to get out of this program. Once you complete this mastery certificate, you’ll be ready to advance your career in geospatial, data science, and other industries involving geospatial data and professional industry certifications.
GIS-D Pathway
The GIS development (GIS-D) pathway is designed for professionals with strong computing backgrounds, including skills and experience in Python, data science, machine learning, and data science application development. If you’re looking to learn more about Python applications for geospatial data, spatial analysis as a methodology, using geospatial technology, spatial analytics, and creating geographic visualizations, this pathway may be the right fit for you.
GIS-A Pathway
The GIS analyst (GIS-A) Pathway is designed for professionals with pre-existing skills and experience with geospatial data and databases, using geospatial technology, conducting spatial analysis, and creating geographic visualizations. If your goal for this program is to learn more about Python computer programming, spatial statistics and analysis, concepts of machine learning, data science fundamentals, and spatial data science application development, this pathway may be the right option for you.
What You’ll Learn
Though there are two pathways in this mastery certificate, the core courses are required for both options and will provide you with the core computing skills you need for spatial data science applications. Throughout this program, you’ll gain skills in Python computer programming, geospatial technology, spatial analysis, data science fundamentals, geographic visualization, spatial data science application development, and more.
How You’ll Learn
This mastery certificate consists of three core courses, one elective with four courses to choose from, and a capstone course. There is also a prerequisite Intro to Python course required for the GIS-A pathway, though you can skip that course if you’re able to demonstrate an equivalent skillset. All of the courses in this program are fully online, and all of the courses include synchronous sessions aside from the Intro to Python course. Each course has a different focus and distribution of instructional time and practical application, but each will include hands-on, experiential learning that will prepare you to use your newly acquired skills in real-world applications.
Skills You Walk Away With
By the time you complete this mastery certificate, you will be able to:
- Demonstrate how to write basic Python or R computer code for the unique aspects of geospatial data that utilizes variables, data types, statements, and expressions.
- Operate relevant geospatial technology for spatial data science applications.
- Utilize Python code with commercial Python (ArcPy, ArcGIS API for Python), open-source Python (GeoPandas), or R spatial data science libraries and development environments.
- Conduct spatial analysis within Python spatial data science libraries by problem-solving via spatial statistics and machine learning including prediction, suitability, patterns and clustering, deep learning.
- Create geospatial data visualizations in Python using spatial data science libraries.
- Synthesize Python spatial data science libraries for spatial data science application development.