Explores principles and applications of data ethics within a broader social framework that prioritizes conversations about policy, regulatory frameworks, accountability, transparency, and governance models. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities.

For students advanced beyond the level of ENCW 2600. Involves workshop of student work, craft discussions, and relevant reading. May be repeated with different instructor. For instructions on how to apply to this class or more details, please visit our program website at creativewriting.virginia.edu/ugrad.

Caring well for an aging population is among the greatest challenges facing both the United States and the world. Significant gaps persist between the health and social systems that older adults need, and those to which they have access. This course uses a multidisciplinary approach--encompassing history, public health, ethics, the social sciences, and literature--to explore these gaps, their impact, and their meaning.

The course is designed to not only teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Examples might include tailoring messages based on the audience or shaping visualizations to follow a story-line. Content on the development of interactive plots and dashboards will also be included.

This poetics seminar, designed for students in the English Department's Area Program in Poetry Writing but open to other students on a space-available basis, is a close readings course for serious makers and readers of poems. Seminar topics vary by semester. For more details on this class, please visit the department website at creativewriting.virginia.edu/ugrad.

Designed not only to teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Lays the foundation for more advanced topics in the Data Design domain. Content on the development of interactive plots and dashboards will also be included.

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.

Moves deeper into current best practices around data engineering in industry. Topics will review basic data collection, ingestion, processing, and storage, moving beyond to data governance, security, pipeline orchestration, monitoring and maintenance, optimization, and documentation. Relies heavily on DevOps principles of automation, continuous improvement, and an understanding of the entire software/data lifecycle.

Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

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.