Introduction#

In the course Big Data for 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 wildfires.

The course is structured around six main data types and related methods. Specifically, we will:

  1. explore census data and use machine-learning methods to fill data gaps and derive new information and knowledge;

  2. better understand how you can use Earth Observation data to classify land cover.

  3. assess how droughts affect us through using remote sensing data;

  4. understand how to use open-source and Volunteered geographic information (VGI);

  5. apply nature language processing techniques to identify the occurrence of floods through social media data;

  6. learn how the right visualization can help us to extract the right messages from our data.

In the first week, we will provide a crash course in Python that will be used throughout the remainder of the course. You are not expected to have any prior knowledge in Python before this course.

Objectives#

The key objectives of this course are:

  • to know how and when big data can be used to solve sustainability problems.

  • to gain a better understanding of methods and tools to analyze big data.

Teaching methods#

This course will be a combination of lectures and tutorials. Each week starts with an introductory lecture to the method and/or data type that will be applied and/or analyzed in that particular week. During the lecture, students will gain the required theoretical knowledge to apply the methods during the two tutorials. The tutorials are each a 90 minute computer practical in which you will develop the skills and knowledge to work with the method and/or data-type through a hands-on assignment.

Methods of assessment#

There will be four graded assignments and a multiple-choice exam in the final week. The weekly assignments will account for 60% of the grade, and the final exam will account for 40% of the grade. The weekly assignments will be made in groups of two, whereas the final exam will be individual.

You must pass both elements (5.5 or higher).