📅 Completion Date: August 2023
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Grade achieved (based on assignment): 96.32%
📝 Key Learnings:
- Understanding spatial data types (vector vs raster)
- Using coordinate systems and map projections
- Performing spatial analysis (buffering, overlay, geoprocessing)
- Introduction to ArcGIS tools and workflows
- Creating and interpreting thematic maps
📅 Completion Date: August 2023
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Grade achieved (based on assignment): 93.81%
📝 Key Learnings:
- Understanding data formats: shapefiles, geodatabases, raster data
- Metadata and documentation for spatial datasets
- Principles of cartographic design and data visualization
- Assessing data quality, accuracy, and uncertainty
- Best practices for managing and sharing GIS data
📅 Completion Date: August 2023
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📝 Key Learnings:
- Navigating the QGIS interface and managing spatial data
- Working with vector and raster datasets
- Spatial analysis: buffering, overlay, and geoprocessing
- Map design, symbology, and labeling for effective cartography
- Automating workflows with QGIS tools and plugins
📅 Completion Date: September 2023
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📝 Key Learnings:
- Introduction to ArcGIS and Erdas Imagine workflows
- Handling vector and raster data for basic GIS analysis
- Performing remote sensing tasks such as classification and change detection
- Map creation and layout design for presentation
- Practical exercises applying remote sensing to environmental datasets
📅 Completion Date: September 2025
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📝 Key Learnings:
- Fundamentals of digital image processing
- Image filtering, transformations, and edge detection
- Working with color spaces and histograms
- Applying OpenCV functions for feature extraction
- Practical applications in remote sensing & computer vision
📅 Completion Date: September 2025
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Language: Python
📝 Key Learnings:
- Downloading and managing different types of satellite remote sensing data (free sources)
- Understanding remote sensing concepts and real-world applications
- Pre-processing satellite data using R and QGIS
- Performing unsupervised and supervised classification of satellite data
- Applying machine learning algorithms to remote sensing analysis in R
- Conducting habitat suitability mapping with remote sensing + ML
- Using additional open-source tools such as Google Earth Engine and SNAP for RS data analysis
📅 Completion Date: in progress
Course Website
Language: Python
📝 Key Learnings:
- Understanding basic programming concepts in Python (variables, loops, conditionals)
- Writing clean, readable code (good variable names, comments, structure)
- Version control with Git & GitHub for tracking code and collaborating
- Using tools like Jupyter Notebooks / cloud environments for reproducible scientific analysis
- Data analysis basics: loading, manipulating, visualizing data with Python
📅 Completion Date: in progress
Course Website
Language: Python
📝 Key Learnings:
- Handling geospatial data using tools like GeoPandas: reading/writing, CRS handling, spatial joins
- Working with geometry objects (points, lines, polygons) using Shapely
- Spatial queries, geocoding, overlay analysis
- Visualisation of spatial data: static maps, interactive mapping
- Exploring network analysis (e.g. with OSMnx, NetworkX) and handling raster data
- Good programming practices, documentation & reproducible workflows (Git, online repos)