● 4.5 hours of on-demand video ● 1 article ● Full lifetime access ● Access on mobile and Laptop ● By Edwin Bomela
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.
We be building a predictive model that we will use to do a further analysis, on our data and 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 dashboard that 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 to serve 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
WHAT YOU WILL LEARN
● Data Visualization
● Data Analysis
● Data Transformation and Manipulation
● Python and Bokeh
● Geospatial Machine Learning
● Geo Mapping
● Python Programming
● Creating Dashboards
WHO THIS COURSE IS FOR
● Python Developers at any level
● GIS Developers at any level
● Developers at any level
● Machine Learning engineers at any level
● The curious mind
2. Setup and Installations
3. Data Preparation
4. Data Visualization
5. Machine Learning
6. Building the Dashboard
7. Creating the Dashboard Server
8. Project Source Code
Edwin Bomela is a Big Data Engineer and Consultant, 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. He is currently a 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.