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New Course: Google Tools for GIS Applications

I am pleased to announce a new course titled “Google Tools for GIS Applications“. This course is an overview of Google Cloud Platform tools, analytical tools, and mapping API’s that may be of interest to geospatial professionals.  The course is broad rather than deep.  My goal is to show you how to get started with many different products with an emphasis on geospatial applications.  In many cases there are existing courses that cover the details but with little information on geospatial applications and this course is intended to fill in those gaps.

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What can GeoPandas do for you?

GeoPandas is a Python package that extends the very popular Pandas package with the ability to read, analyze, and visualize geospatial data.  Like Pandas, GeoPandas is generally used within a Jupyter notebook which provides a powerful framework for documenting your analysis workflow.  Over the past few years I have moved increasingly towards using GeoPandas for any analysis project as I have found it to have many advantages over traditional desktop GIS approaches. (See my blogpost Geospatial Data Science vs GIS for more information)

Some basic knowledge of Python is required to use GeoPandas however you do not need to be an expert programmer to take advantage of GeoPandas. Python provides the syntax necessary to call GeoPandas methods but most GeoPandas code will be very simple and easy to read and understand.  As such it is a great way to learn to use Python. Continue reading “What can GeoPandas do for you?”

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New Course: Geospatial Data Science: Statistics and Machine Learning I

I am pleased to announce the availability of a new course “Geospatial Data Science with Python: Statistics and Machine Learning I“. This course is about statistical analysis of vector data and machine learning using vector data.   Statistical inference and machine learning are closely related and use a similar set of methods but ultimately have different goals.  Statistical inference is used to make inference from a sample to a population and its goal is generally to improve understanding of the underlying processes of interest, while the goal of machine learning is to use a set of training data to “teach the machine” to make predictions about new observations where the truth is not known.

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