A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.

A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.

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).

Course investigates practical challenges policy researchers face conducting impact evaluations. Develop capacity to replicate prominent empirical research using experimental & quasi-experimental methods & present results in compelling, accessible formats.Course primarily uses R (No prior exp. w/R expected). Course assumes prior grad-level instruction in experimental & quasi-experimental methods and Batten MPPs likely have completed RMDA II.

Graduate-level poetry writing workshop for advanced writing students. A weekly 2.5 hour workshop discussion of student poems. For more details, visit our program website at creativewriting.virginia.edu.

Covers advanced theoretical concepts for deep neural networks. Topics include convolutional neural networks and their design principles, encoder-decoder architectures, recurrent neural networks, transformers, bounding box detection, image segmentation, generative adversarial networks, diffusion models, etc. Using open-source Python libraries such as NumPy, TensorFlow, and Keras, to understand how theoretical concepts are implemented.

Introduces ways that data and information have historically been constructed in different realms--from medicine to public health to computing--to shed light on the power relationships embedded in some of our present-day and near-future tools, systems, and economic relationships. Will use a historical lens, as well as methods from STS, to give an introduction to how data and power interact in people's lives.

The Applied Policy Project (APP) is the capstone event of the MPP program, an independent analytical project for each student. Divided over two semesters, APP I provides students with the opportunity for a semester of research and information gathering in the policy field of the student's external client.

Transition into principal investigators and generators of data science-based knowledge. Develop practical skills necessary to conduct high quality data science research, advance development into producers and critical consumers of research, and further development into professional data scientists broadly defined. Research based career topics covered: time management, research products, types of research positions, and grant writing.

Content for this course includes the purposes and nature of theory in educational administration and the application of organization theory to education. Theories of leadership, organizations, decision-making, communication, climate, conflict, change process, and motivation are included.