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Spatial Data Visualization and Machine Learning in Python
( 6 Reviews )
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Introduction 00:14:00
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Python Installation 00:03:00
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Installing Bokeh 00:04:00
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Data Preparation 00:24:00
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Creating the barchart Part 1 00:10:00
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Creating a Line Chart 00:12:00
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Creating a Doughnut Chart 00:22:00
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Creating a Magnitude Plot 00:31:00
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Creating a Geo Map Plot 00:20:00
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Creating a Grid Plot 00:12:00
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Data Pre-processing 00:21:00
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Building a Predictive Model 00:21:00
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Building a Prediction Dataset 00:07:00
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Resources
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Installing Visual Studio Code 00:01:00
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Creating the Project and Virtual Environment 00:08:00
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Building and Running the Server 00:12:00
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Assignment – Spatial Data Visualization and Machine Learning in Python 06:05:00
Course Overview
Spatial data visualisation and machine learning with Python constitute a vital skill set in today’s data-driven world. Understanding spatial data—information connected to particular geographic locations—becomes increasingly important for a variety of industries as the world grows more interconnected. When people use Python to visualise spatial data, they can uncover intricate patterns and insights that are missed by more conventional methods. Additionally, the combination of machine learning methods and spatial data enhances the ability to make decisions by facilitating trend forecasting, resource allocation optimisation, and predictive analytics.
Students who enrol in the “Spatial Data Visualisation and Machine Learning in Python” course will graduate with a strong toolkit that will enable them to efficiently navigate and utilise spatial data. Participants will explore data preparation techniques, including cleaning, formatting, and preprocessing spatial data, after reviewing the fundamental knowledge covered in sections one and two. The course then explores the capabilities of Python visualisation tools in sections three and four, demonstrating how to visually represent and analyse geographic data. In Section 5, machine learning algorithms are introduced and used to analyse spatial data, which forms the core of the course. As a final step in their learning process, students will build a dashboard—a topic covered in sections six and seven—to effectively present their findings. Learners receive a thorough understanding of the entire process with a final section on gaining access to the project source code.
Set out on a transformative journey where machine learning, spatial data visualisation, and Python come together to unlock geographical insights and shape the future. Through practical projects and in-depth instruction, this course will teach you the fundamentals of spatial intelligence, enabling you to fully utilise geographic data in the digital age.
Key features
- E-learning and E-assessment system with flexibility.
- Learners get interactive resources.
- Expert advice from qualified professionals
- Globally recognised certificate.
- 14 days refund guarantee.
- 24/7 Tutor Support
Learning Outcome
- Use Python to analyse spatial data to solve problems and make well-informed decisions
- Utilise machine learning methods to conclude a variety of geographical datasets
- Create visualisations that efficiently convey complicated spatial information
- Gain expertise in preparing data for tasks involving spatial analysis and visualisation
- Make dynamic dashboards that display useful information gleaned from the examination of spatial data
- Make extensive use of project source code for ongoing education and implementation
Course Details
- Learn at your own speed with affordable, high-quality E-learning content.
- When you finish the course, you will be given a certificate of completion.
- Enhance your resume from a globally recognised Accredited Qualification.
- You’ll discover how effective salespeople use a researched and proven technique to boost their careers.
- You’ll be able to successfully implement several practical sales strategies and have a deeper understanding of your consumers.
Though the “Spatial Data Visualisation and Machine Learning in Python” course is accessible to all, the following categories of learners find it as most helpful training:
- Data Analysts seeking to expand their skills into spatial analytics
- GIS Professionals looking to integrate Python for data analysis and visualisation
- Data scientists eager to specialise in spatial data and machine learning
- Programmers/Developers interested in spatial data and its applications
- Geographers/Planners wanting to incorporate data analysis into their field
- Students/Researchers pursuing studies involving spatial data and analytics
- Professionals in Environmental Sciences seeking to analyse geospatial data
- Business Analysts interested in leveraging spatial insights for decision-making
- Section 01: Introduction
Introduction - Section 02: Setup and Installations
Python Installation
Installing Bokeh - Section 03: Data Preparation
Data Preparation - Section 04: Data Visualization
Creating a Bar Chart
Creating a Line Chart
Creating a Doughnut Chart
Creating a Magnitude Plot
Creating a Geo Map Plot
Creating a Grid Plot - Section 05: Machine Learning
Data Pre-processing
Building a Predictive Model
Building a Prediction Dataset - Section 06: Building the Dashboard
Adding predicted data to our plots – Part 1
Adding predicted data to our plots – Part 2
Adding predicted data to our plots – Part 3
Adding the Grid Plot - Section 07: Creating the Dashboard Server
Installing Visual Studio Code
Creating the Project and Virtual Environment
Building and Running the Server - Section 08: Project Source Code
Resources
Learners must submit a comprehensive summarised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the “Spatial Data Visualisation and Machine Learning in Python” certificate.
An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.
QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions, including your progression routes into further higher education. It is a non-accredited course.
There are no formal requirements needed for this “Spatial Data Visualisation and Machine Learning in Python” course. So learners do not require any prior qualifications to enrol in this course.
Spatial Data Visualisation and Machine Learning in Python course helps the following professionals to perform best in their job roles:
- Data Scientist (Spatial Analysis): £40,000 – £90,000 per annum
- Geospatial Analyst: £25,000 – £50,000 per annum
- Machine Learning Engineer: £45,000 – £100,000 per annum
- GIS (Geographic Information Systems) Specialist: £30,000 – £60,000 per annum
- Data Visualisation Specialist: £35,000 – £70,000 per annum
- Spatial Data Engineer: £35,000 – £80,000 per annum
Course Reviews
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Course Info
Lessons : 23
Assignments : 2
Duration : 16 hours, 42 minutes
Access : 1 year
Enrol now, pay later in easy instalments(Interest free) with Clearpay.
