Description
Curriculum
Instructor
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Master Adobe Photoshop CC 2024 without any previous knowledge. Learn the newest AI tricks to get fast results like a pro
What you’ll learn
- Automate tasks on their computer by writing simple Python programs.
- Programmatically generate and update Excel spreadsheets.
- Crawl web sites and pull information from online sources.
- Use Python’s debugging tools to quickly figure out bugs in your code.
- Write programs that can do text pattern recognition with “regular expressions”
- Parse PDFs and Word documents.
- Write programs that send out email notifications.
- Programmatically control the mouse and keyboard to click and type for you.
This course is a detailed and easy tutorial to get you all setup and going with the use of LearnPress LMS Plugin. It is a free and simple plugin to help you create an Online Courses Website step by step. The tutorial guides you through the configuration of the plugin, creation of Courses, Lessons, Quizzes, and finally guides you on how to boost up your Website with Premium LearnPress Add-ons brought to you by ThimPress (creator of LearnPress). It also shows how you could configure additional items like the course layouts and featured images …
A series of Videos from ThimPress, give you a detailed tutorial to create an LMS Website with LearnPress – LMS & Education WordPress Plugin.
- 3 Sections
- 24 Lessons
- 20 Weeks
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- Welcome, Orientation, and Learning SetupOfficial component Credits Suggested duration Assessment anchor LMS onboarding layer added to support delivery of the QCTO-aligned programme Not separately credited; supports readiness for all assessed sections Week 1 Setup checklist submission This opening section establishes the learning contract, course workflow, software environment, and ethical posture required for the rest of the programme. It orients learners to the logic of the course, the role of self-directed practice, and the evidence they will need to produce over six months.5
- Big Data Analytics in Spatial IntelligenceOfficial component Credits Suggested duration Assessment anchor 900037-000-00-KM-01 8 credits Weeks 2–7 Lab 1 — Municipal or public-service spatial scan This section builds the spatial-data foundation of the course. Learners move from understanding what spatial intelligence is to handling projections, metadata, exploratory analysis, hotspot detection, predictive reasoning, and cloud-oriented workflows. By the end, the learner should be able to approach a spatial problem methodically and explain what data, methods, and governance issues matter before automation is attempted.11
- 2.1What is spatial intelligence and why it matters35 Minutes
- 2.2Spatial data types: vector, raster, tabular, sensor, and imagery data50 Minutes
- 2.3Coordinate systems, projections, and spatial reference basics55 Minutes
- 2.4Data quality, metadata, and spatial data governance45 Minutes
- 2.5Spatial databases and large-scale geospatial datasets55 Minutes
- 2.6Exploratory spatial data analysis and descriptive mapping60 Minutes
- 2.7Hotspot analysis, clustering, and pattern detection70 Minutes
- 2.8Predictive thinking in spatial analysis60 Minutes
- 2.9Cloud and scalable workflows for geospatial data45 Minutes
- 2.10Mini lab: service-delivery hotspot dashboard concept90 Minutes
- 2.11Lab 1 — Municipal or public-service spatial scan20 Minutes3 Questions
- Geospatial Artificial IntelligenceOfficial component Credits Suggested duration Assessment anchor 900037-000-00-KM-02 8 credits Weeks 8–12 Lab 2 — GeoAI workflow design This section translates AI concepts into geospatial practice. Learners study how machine learning and deep learning support spatial feature extraction, imagery interpretation, object detection, evaluation, and responsible deployment. The focus is conceptual fluency plus workflow design, not blind model worship.10
- 3.1Introduction to GeoAI and spatial machine learning40 Minutes
- 3.2Supervised, unsupervised, and deep learning in spatial contexts60 Minutes
- 3.3Feature engineering for geospatial datasets55 Minutes
- 3.4Imagery fundamentals for AI workflows50 Minutes
- 3.5Labelling, annotation, and training sample preparation70 Minutes
- 3.6Object detection concepts for satellite and aerial imagery70 Minutes
- 3.7Model evaluation: precision, recall, F1, and error analysis60 Minutes
- 3.8Bias, ethics, explainability, and responsible GeoAI45 Minutes
- 3.9Mini lab: building an object-detection workflow plan90 Minutes
- 3.10Lab 2 — GeoAI workflow design1 Question
Prof. Malusi Sibiya
0 Students1 Course
Target Audiences
- GIS and geospatial learners
- data analysts entering spatial analytics
- municipal or public-sector data teams
- infrastructure and planning professionals
- early-career GeoAI / spatial application developers
Requirements
- National Senior Certificate, NQF Level 4
- National Certificate (Vocational), NQF Level 4
- National N Diploma, NQF Level 5
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