Spatial Data Visualization and Machine Learning in Python

Spatial Data Visualization and Machine Learning in Python

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Building an Analytics Dashboard to Analyze and Predict Quakes using Bokeh and Python

Updated on Sep, 2026

Programming, Data Science, Python

Duration - 4.5 hours

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Welcome to the 'Spatial Data Visualization and Machine Learning in Python' course.In this course we will be building a spatial data analytics dashboard using bokeh and python.

Bokeh is a very powerful data visualization library that is used for building a wide rangeof interactive plots and dashboards using the python programming language.It also converts python code into html and JavaScript code, which allows plots to behosted on servers and displayed in web browsers.

We be building a predictive model that we will use to do a further analysis, on our dataand plot it's forecast results alongside the dataset that we will be focusing on.

We will be visualizing our data in a variety of bokeh charts, which we will explore in depth.Once we understand each plot in depth, we will be equipped with the knowledge to build a dashboardthat we will use to analyze our data.

And once we have built our dashboard, we will then create a lightweight server that we will use toserve our dashboard and make it accessible via a URL.

You will learn how to visualize spatial data in maps and charts

You will learn data analysis using jupyter notebook

You will learn how to manipulate, clean and transform data

You will learn how to use the Bokeh library

You will learn machine learning with geospatial data

You will learn basic geo mapping

You will learn how to create dashboards

Who this course is for:

Data Transformation and Manipulation

Geospatial Machine Learning

Basic Understanding of Python

Little or no understanding of GIS

Basic understanding of Programming concepts

Basic understanding of Data

Basic understanding of what Machine Learning is

Check out the detailed breakdown of what’s inside the course

Big Data Engineering and Consulting, involved in multiple projects ranging from Business Intelligence, Software Engineering, IoT and Big data analytics. Expertise are in building data processing pipelines in the Hadoop and Cloud ecosystems and software development.

Currently consulting at one of the top business intelligence consultancies helping clients build data warehouses, data lakes, cloud data processing pipelines and machine learning pipelines. The technologies he uses to accomplish client requirements range from Hadoop, Amazon S3, Python, Django, Apache Spark, MSBI, Microsoft Azure, SQL Server Data Tools, Talend and Elastic MapReduce.

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