CE-ITCS-3001 - Open-Source Python for Spatial Statistics and Machine Learning

Course Description

Finding the spatial data science library that best fits your needs is an important part of streamlining your workflow and making sure you’re using your time and resources effectively and efficiently. In this course, you’ll explore the use of the open-source project GeoPandas, including how to utilize it for spatial analysis as well as analytical methods you can use to provide insight based on larger volumes of data.

Is This Course For You?

This course 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. This course is one of the five elective options for the Spatial Data Science Applications Mastery Certificate and is part of the GIS Analyst (GIS-A) track, which is designed for professionals who already have prior experience and skills pertaining to geospatial data and databases, using geospatial technology, conducting spatial analysis, and creating geographic visualizations. This course in particular is ideal for you if you’re looking for a sustainable, free, and open-source spatial data science option and analytical methodology specifically focused on working with larger volumes of geospatial data to provide insight. If you’re pursuing the mastery certificate we strongly recommend you take this course after finishing all three core courses.

What You’ll Learn

In this course, you’ll learn how to utilize GeoPandas open-source spatial data science libraries for spatial analysis, and you’ll cover topics such as advanced spatial statistics and machine learning. Throughout this course, you’ll practice writing new code as well as modifying existing code using open-source libraries to conduct spatial analysis using spatial statistics and machine learning core concepts. You’ll work towards creating a Jupyter notebook and accompanying source code files in GitHub manually written, based on existing code, or AI-generated code with documentation that demonstrates your ability to write or modify GeoPandas code that can perform tasks such as spatial analysis using prediction, suitability, patterns, and clustering. This project can be used as an addition to your professional portfolio to display the skills you’ve gained throughout the course.

How You’ll Learn

In this ten week fully online course, you’ll have required synchronous sessions as well as asynchronous work to complete on your own time. To learn more about the time of the required synchronous sessions, click the “+” icon next to the available course dates. This is a hands-on course that will involve coding exercises where you’ll practice using the skills you’re learning and get feedback and guidance from your instructor.

Skills You Walk Away With

By the end of this course, you will be able to:

• Write Python code that can use GeoPandas.
• Explain spatial statistics and machine learning concepts in relation to open-source libraries.
• Implement Python code that can conduct analysis using prediction, suitability, patterns, and clustering.
• Identify and modify existing open-source Python Code that can be incorporated into a spatial data science workflow.
• Demonstrate how to identify, install, and combine several open-source spatial data science and related analytical Python libraries for spatial data science workflows.

Course Outline

Finding the spatial data science library that best fits your needs is an important part of streamlining your workflow and making sure you’re using your time and resources effectively and efficiently. In this course, you’ll explore the use of the open-source project GeoPandas, including how to utilize it for spatial analysis as well as analytical methods you can use to provide insight based on larger volumes of data.

Is This Course For You?

This course 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. This course is one of the five elective options for the Spatial Data Science Applications Mastery Certificate and is part of the GIS Analyst (GIS-A) track, which is designed for professionals who already have prior experience and skills pertaining to geospatial data and databases, using geospatial technology, conducting spatial analysis, and creating geographic visualizations. You can learn more about which track is best for your career trajectory and goals here [link to mastery certificate page]. This course in particular is ideal for you if you’re looking for a sustainable, free, and open-source spatial data science option and analytical methodology specifically focused on working with larger volumes of geospatial data to provide insight. If you’re pursuing the mastery certificate we strongly recommend you take this course after finishing all three core courses.

What You’ll Learn

In this course, you’ll learn how to utilize GeoPandas open-source spatial data science libraries for spatial analysis, and you’ll cover topics such as advanced spatial statistics and machine learning. Throughout this course, you’ll practice writing new code as well as modifying existing code using open-source libraries to conduct spatial analysis using spatial statistics and machine learning core concepts. You’ll work towards creating a Jupyter notebook and accompanying source code files in GitHub manually written, based on existing code, or AI-generated code with documentation (comments) that demonstrates your ability to write or modify GeoPandas code that can perform tasks such as spatial analysis using prediction, suitability, patterns, and clustering. This project can be used as an addition to your professional portfolio to display the skills you’ve gained throughout the course.

How You’ll Learn

In this ten week fully online course, you’ll have required synchronous sessions as well as asynchronous work to complete on your own time. To learn more about the time of the required synchronous sessions, click the “+” icon next to the available course dates. This is a hands-on course that will involve coding exercises where you’ll practice using the skills you’re learning and get feedback and guidance from your instructor.

Skills You Walk Away With

By the end of this course, you will be able to:

• Write Python code that can use GeoPandas.
• Explain spatial statistics and machine learning concepts in relation to open-source libraries.
• Implement Python code that can conduct analysis using prediction, suitability, patterns, and clustering.
• Identify and modify existing open-source Python Code that can be incorporated into a spatial data science workflow.
• Demonstrate how to identify, install, and combine several open-source spatial data science and related analytical Python libraries for spatial data science workflows.

Notes

Applies Towards the Following Certificates

• Spatial Data Science Applications : GIS-Analyst Elective Track