Course Content & Exit Level Outcomes

Full Time Students (8 hours a day 5 days a week)

Spatial Intelligence Data Science - SIDS (SP-210604) NQF 5 – Credits 56

NQF & Credits
Purpose of this module
Mathematics for Geocomputation
NQF LEVEL 5 Credits 12

The purpose of this module is to provide the learner with the mathematical knowledge and skill set required to work with spatial intelligence data. In general, the learner should be familiar with high school mathematics. It is based on logic and set theory, which are the foundations of most modelling techniques and programming languages as syntactic constructs for expressing propositions, predicates, and inferring conclusions from given or assumed facts.

Digital Transformation
NQF Level 5, Credits 8
This module examines the fundamental components of information and communications technology (ICT) as well as toolkits. It investigates how technologies such as hardware, cloud, cybersecurity, and fusion technology enable the use of artificial intelligence (AI), augmented reality (AR), and the internet of things (IoT and others) in the private and public sectors.
Introduction to Data Acquisition for Spatial Intelligence
NQF Level 5, Credits 12
In this module, the learner will interact with various data sources and collection methods to obtain a dataset, such as configuring an API, the internet, and a database, among others.
Management of Data in Geospatial Database
NQF Level 6, Credits 8
Application development about creating computer programs to perform certain business tasks. The process is about helping businesses automate their processes and increase efficiency. Each application-building process follows the same steps: gathering requirements, designing prototypes, testing, implementing, etc. Whether it is a simple configuration to customize the application, the principle of application development applies. Inevitable Application development is part of a Geodata Scientist at some point in their career.
Spatial Intelligence Monitoring and Reporting
NQF Level 5, Credits 8
This module delves into the concepts of data visualization and visualization tools for situational awareness. The science of visual communication is at the heart of data visualization. Visual aids stimulate human intelligence by assisting in the deciphering of information that would otherwise elude the brain. Visual aids help to drive the story behind the data and can reveal patterns, trends, and relationships that would otherwise be missed.
Spatial Intelligence Cloud Computing
NQF Level 5, Credits 8
This module is intended to examine the fundamental elements of cloud computing and available solutions, as well as their application in the geospatial industry. The concept of cloud computing is gaining popularity and has roused the interest of both private and now government enterprises. Even though technology is becoming more affordable and accessible, organizations must continue to develop their strategies and test their options to determine whether this will be an effective way for them to deliver applications and services.
Spatial Intelligence Data Science- SIDS Upon qualifying learners who complete this skills programme will be able to:
  1. Methodically assess and solve problems related to digital transformation in general for the fourth industrial revolution.
  2. Demonstrate knowledge and comprehension of the role of information technologies in the digital economy.
  3. Understand number systems and complex number concepts, recall the meaning of terms such as real part, and relate complex numbers
  4. Use statistical and mathematical analytic skills to solve big data problems in a geospatial context.
  5. Acquire, process, perform, and analyse data management issues as they relate to business problems.
  6. Conduct investigations of broadly defined problems; locate, search, and select relevant unstructured data from codes, databases, and literature; analyse and interpret results to provide valid conclusions.
  7. Demonstrate knowledge and understanding of the impact of spatial intelligence activities on society, the economy, the industrial and physical environment, and address issues.
  8. Demonstrate knowledge and skills for effectively implementing cloud computing for enterprise solutions.
  9. Demonstrate the technical ability to build data capturing and reporting tools.

      Produce insights and visual reports for business to solutions.

Advanced Spatial Intelligence Data Scientist (SP-210603) NQF 5 – Credits 40

NQF & Credits
Purpose of this module
Big Data Analytics in Spatial Intelligence
NQF Level 5, Credits 8
In this module, the learner will use the distributed architecture to analyse and display large volumes of data, and spatial analytics tools will help reveal complex spatial patterns. In general, Big Data Analytics is concerned with ingesting real-time data from sensors, social media feeds, or IoT systems and transforming this massive data into manageable and actionable insights.
Geospatial Artificial Intelligence
NQF Level 5, Credits 8
This module examines the power it takes for a distributed computing framework for processing and analysis of big data. The leaners will use distributed computing capabilities such as aggregation, regression, detection, clustering tools, how to visualize, understand, and gain insights that may otherwise be hidden such as patterns, trends, and anomalies.
Spatial Intelligence Augmented and Virtual Reality
NQF Level 5, Credits 8
This module examines concepts and techniques of simulated or virtual worlds. The learners will be exposed to the creation of artificial geographies for use in case studies and the real world.
Application Development for Spatial Intelligence
NQF Level 5, Credits 8
In this module, the learner will gain skills to develop Geographic centred Applications to perform certain business tasks. The leaner will be exposed to gathering requirements, designing prototypes, testing and implementing the application.
Programming for Geospatial-Intelligence
NQF Level 5, Credits 8
This module will introduce geospatial developers and data scientists to a variety of programming languages. It covers the fundamentals of scripting in GIS and compares its applications. It explains how to manage a wide range of Geospatial Intelligence data, publish, update, and build sophisticated analytical models, and automate mission-critical workflows using each language and available APIs.
Advanced Spatial Intelligence Data Scientist - ASIDS Upon qualifying learners who complete this skills programme will be able to:
  1. Demonstrate the in-depth understanding of application development and development cycle.
  2. Build applications with spatial capabilities
  3. Understand the programming languages used in geospatial intelligence and modelling
  4. Explore big data and analyse it.
  5. Explore the Geospatial artificial intelligence and analysis process
  6. Understand basic concepts, principles of virtual reality.
  7. Create augmented reality web scenes.