Big Data for Sustainability Sciences

Data is everywhere around us. But how can we make use of all this data to answer questions in sustainability sciences? How can twitter data help us to understand where and how severe people are affected by floods? Or how can we use earth observation data to classify land use?

In the course Big Data in Sustainability Sciences, you will learn the tools and knowledge to work with large datasets that are widely used within sustainability sciences. This ranges from filling gaps in census data through machine learning approaches, to working with earth observation data to map droughts.

Course overview

• Introduction
• Teachers
• Course schedule

Week 1

• Lecture: Course Introduction
• Tutorial 1: Introduction to Python
• Tutorial 2: Introduction to NumPy and Pandas
• Tutorial 3: Introduction to Data Analysis

Week 2

• Lecture: Introduction to Random Forests
• Tutorial 1: Data Exploration and regression analysis
• Tutorial 2: Random Forest Regression

Week 3

• Lecture: Introduction to Artificial Neural Networks and model validation
• Tutorial 1: Artificial neural networks (ANNs)
• Tutorial 2: Model Validation

Week 4

• Lecture: Earth Observation & Google Earth Engine
• Tutorial 1: Land-cover classification
• Tutorial 2: Drought Detection

Week 5

• Lecture: Big Data in the public domain
• Tutorial 1: Working with OpenStreetMap
• Tutorial 2: Natural Hazard Risk Assessment

Week 6

• Lecture: Social Media and Natural Language Processing (NLP)
• Tutorial 1: Social Media & Natural Hazards
• Tutorial 2: Social Media & Valuation of Landscapes

Week 7

• Lecture: A guide to visualisation
• Tutorial: Visualizing your results

Week 8

• Exam preparation