DATA ANALYSIS AND VISUALIZATION IN SUSTA

Data science is an exciting new field of applied research that takes advantage of the ever-growing volume of data being collected to support of decision-making in both the public and private sectors. Similar to traditional statistical analysis, these new approaches have limits and issues that are important to understand before application to problem solving. This is a full semester course taught in person.  It aims to introduce the common methods used in data science, best practices in data management, and the basic scripting skills required to start analyzing data in R and Python. After introducing foundational scripting and data analysis methods, a case study approach will be used to highlight both what can be accomplished with data analysis and the limits of the data and methods used. Lab exercises will teach basic skills in scripting in Python and R and then move to a common approach for data analysis: adapting existing scripts and software libraries to solve applied data problems.

The requirement to understand the interaction of social and natural systems requires data-driven policy decisions, and the ongoing assessment of policies requires rigorous, reproducible assessments of effectiveness for promoting sustainability. Both requirements can be met in part by data science approaches that are applicable to the natural and social sciences and reproducible in academic and applied settings. Data science techniques have been developed to derive insight from large volumes of available data that are often collected for purposes other than the interests of the data scientist. This flexibility in approach means that the techniques used in data science are well adapted to support gaining insights from data relevant for sustainability science. This course has been designed to introduce these techniques in anticipation of increased use in promoting sustainability.