. . . . "Develop and present a final project using Python in Jupyter notebooks"@en . . . . "Implement basic machine learning techniques for GIS and Earth observation data"@en . . . . "Perform descriptive statistical analyses using Python"@en . . . . "Understand and apply basic Python programming concepts"@en . . . . "Work with geospatial vector, raster, and tabular data in Python"@en . . . . . . . . . . "Basic Machine Learning in GIS and Earth Observation"@en . "Fundamental Programming for Geospatial Data Analysis: LU4"@en . . . . . "Introduction to Python"@en . "Fundamental Programming for Geospatial Data Analysis: LU1"@en . . . . . "Final Project "@en . "Fundamental Programming for Geospatial Data Analysis: LU5"@en . . . . . "Descriptive Statistics in Python"@en . "Fundamental Programming for Geospatial Data Analysis: LU2"@en . . . . . "Geospatial Data Handling in Python "@en . "Fundamental Programming for Geospatial Data Analysis: LU3"@en . . . . . . . . . . . . . . . . . "Decision Maker/Management"@en . . . . . . . . . . . . . . . . "Natural Mapping & Management Generalist"@en . . . . . . . . . . . . . . . . . . "Geospatial Information Visualiser"@en . . . . . . . . . . . . . . . . "Data Analyst/Analysis"@en . . . . . . . . . . . . . . . . "Natural Modelling Vegetation Specialist"@en . . . . . . . . . . . . . . . . "Water and vegetation modelling and management (WRS+NRM"@en . . . . . . . . . . . . . . . . . . . "Planning for Liveable & Resilient Cities"@en . . . . . . . . . . . . . . . . . . . "Making Cities and Land SMART"@en . . . . . . . . . . . . . . . . "Modeler/Modeling"@en . . . . . . . . . . . . . . . . "Natural Monitoring Vegetation Expert"@en . . . . . . . . . . . . . . . . . . "Geospatial Data Engineer"@en . . . . . . . . . . . . . . . . . . "Remote Sensing Specialist"@en . . . . . . . . . . . . . . . . "Natural Mapping & Modelling Conservationalist"@en . . . . . . . . . . . . . . . . "Water and Health modelling and management (WRS+EOS)"@en . . . . . . . . . . . . . . . . . "Geological Remote Sensing/Earth Resources Security"@en . . . . . . . . . . . . . . . . . . "Geospatial Analyst"@en . . . . . . . . . . . . . . . . "Ocean, coastal – delta and inland water modelling and management"@en . . . . . . . . . . . . . . . . . . "3D Geoinformation Engineer"@en . . . . . . . . . . . . . . . . "Inland water resources modelling and management"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M-GEO 5.0"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Spatial Data Science"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Urban & Land Futures"@en . . . . . . . . . . . . . . . . . . . . . . . . "GeoAI"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Resources Security"@en . . . . . . . . . . . . . . . . . . . . . "Disaster Resilience"@en . . . "Course"@en . "29" . "5.0" . "140.0" . "10.0" . "online" . "No"@en . "Yes"@en . "1.0" . "In an increasingly data-driven world, proficiency in data analysis and geospatial data processing has become a crucial skill set. The course “From Statistics to Programming and Machine Learning” is designed to equip students with the fundamental tools and techniques required to navigate the complex landscapes of data analysis, descriptive statistics, geospatial data handling and foundational machine learning techniques using Python. Python, with its extensive libraries and versatility, is the go-to language for professionals in data science, geography, environmental science, urban planning, and beyond.\n\nThis 5 EC course offers a comprehensive introduction to Python, starting with the basics of programming and gradually progressing to more advanced topics like descriptive statistics, data visualisation, and geospatial data processing and Machine Learning. With Jupyter Notebooks, an interactive environment widely used by data professionals, students will gain hands-on experience in writing Python code, analysing data, and visualising results.\n\nThe course is structured into five modules, each focusing on a critical aspect of Python for data analysis. Beginning with Python programming fundamentals, students will build a strong foundation before moving on to descriptive statistics and data visualisation techniques. The course then delves into geospatial data, where students will learn to handle and analyse vector and raster data, followed by a module on advanced manipulation and visualisation of tabular data. The course culminates in an integrated project where students will apply their knowledge to solve a real-world problem, combining geospatial and tabular data for a comprehensive analysis.\n\nBy the end of this course, students will not only have mastered the essential skills in Python programming and data analysis but also be prepared to tackle complex data-driven challenges in GIS and Earth observation."@en . "Fundamental Programming for Geospatial Data Analysis"@en . "Fundamental Programming for Geospatial Data Analysis"@en . "Fundamental Programming for Geospatial Data Analysis"