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).

This course introduces students to key business topics relevant to high technology companies. Students will learn how to understand and interpret financial statements and frame financial decisions, including building a business case. The course will explore typical organizational structures and the roles of business functions. Students will be introduced to business models and other concepts in marketing and business strategy.

This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues surrounding the field of data science and how these issues originate and extend into society more broadly.

Each student or small group will develop a project, be matched with a Global Studies faculty mentor, identify relevant community groups, and spend the semester working on that project. Students will discuss ideas, formulate plans, identify tactics, and engage with important social justice literatures. Importantly, the course will engage with the project of activism itself, which has the potential to replicate systems of inequality.

This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language.