Sustainability Analysis in Python

Sustainability Analysis in Python (5ec)

Industrial ecology relies on the analysis of potentially large datasets from various sources. This course will help students to choose, explore, process, and share datasets. These datasets are often integrated into models, which are a very simplified representation of our world to allow its operationalization for large scales, due to global supply chains, etc. Therefore, it is important to validate models to test their applicability in other contexts (e.g., time or location), and to assess model uncertainties (e.g., due to model choices or uncertainties in the input data).

All these types of analysis will be carried out in Python, a general-purpose and very popular programming language. A programming language like Python can more easily handle large datasets than many other software tools and spreadsheet programmes. It is also highly flexible and can be customized to integrate different industrial ecology methods which otherwise would have to be performed each using a different software tool. In addition, tasks can easily be repeated which saves time, and every step is well documented. This course builds upon and goes beyond the Python skills gained in Earth System Science and Analysis. It includes code optimization, the creation of simple GUIs, and best practices, such as version control, standardized code documentation, and code sharing.

The skills taught in this course are generic and do not focus on a specific type of “sustainability analysis” but will be applied to sustainability-related topics. The analytical, problem-solving, critical-thinking, and programming skills gained in this course are transferable skills, which are in high demand in the job market of industrial ecology and beyond.

This course is part of the joint degree MSc Industrial Ecology, offered by Leiden University and Delft University of Technology. More information about the course is published in the online study guide of Leiden University