Geography matters! We'll explore theories & cases to better understand issues as the struggle over the ocean/other public commons, the role of sacred spaces in Indigenous communities, how migrants make a place for themselves in their new homes, economic resilience and how capital, goods and people circulate in the economy, and more. This is a good introduction to themes raised in Global Studies.

In this course, students gain experience applying global perspectives, as well as research methods and techniques, to one of several real-world issues. Team-taught, the course allows students to choose a path that includes a methodological foundation, a deep dive into a particular method, a chance to practice a useful skills related to Global Studies professions, and culminating in the applied research project.

Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

This course takes an ethnographically informed approach to the question of how to understand corruption by examining practices of and perspectives on corruption from across the globe - including the so-called Global North. It aims to encourage students to 1) critically assess assumptions at the heart of international anti-corruption discourses; 2) examine tensions between global discourses of corruption and local practices; 3) compare and contrast corruption between different localities.

Introduction to core data science concepts and skills, including computing environments, visualization, modeling, and bias analysis. Think like a Data Scientist as you engage through lectures, discussions, labs, and guest talks while applying learning in a guided semester-long project. Concludes with an independent project to reinforce and extend skills.

Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be covered in depth. Additionally, project management skills such as Git and Github will be covered.

Will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and foundation knowledge in terms of computational environments. The content will lay the groundwork for more advanced Systems Domain courses in the major.

In this course, students will learn how to trace the "causal chains" from such actions/inactions to various ecosystem, social, and economic outcomes and to measure and value those outcomes. We will consider the philosophical/ethical underpinnings of the Ecosystem Services framework, use computer mapping and other software tools for evaluation, and review current applications of the framework by private and public sector entities.

Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and network centrality.

Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable and blazingly fast. Fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames) along with Tools for data processing, storage, and retrieval, including Amazon Web Services (AWS).