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.
This 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.
The 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.
By 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.