A continuation of DANCE 120. Also taught at Mount Holyoke and Smith.

Modern Dance technique after the Humphrey/Limon style. Floor work, center and locomotor exercises geared to enhance the student's strength, coordination, balance, flexibility, spatial awareness, rhythmic understanding and dynamics of movement. Attention is given to isolated movements and full combinations across the floor.

This course will build on students' previous foundations in probability, statistical inference, and linear regression. An introduction to generalized linear models (GLMs) and multilevel (mixed effects/hierarchical) models will be followed by additional advanced topics at the discretion of the instructor. These will include special cases of GLMs and multilevel models and may also consider measurement of latent variables (e.g. factor analysis, IRT).

This course covers various approaches for network inference and delves into the following questions: How do networks we observe emerge? Under what conditions do they change (or not)? What are the network outcomes individuals get based on their structural positions and roles? How do information and resources move from one spot to another within and beyond networks? Where does the flow stop? What are the mechanisms that lead to changes to networks?

As social scientists, we not only want to identify correlations and patterns in data but also want to explain why those patterns exist. In this course, students will first learn the fundamentals of causal inference, including key concepts and the directed acyclic graph (DAG) as a broadly applicable modeling framework. From there, the course will introduce and proceed through tutorials on a variety of causal inference approaches.

With the recent explosion in availability of digitized text, social scientists increasingly are turning to computational tools for the analysis of text as data. In this course, students will first learn how to convert text to formats suitable for analysis. From there, the course will introduce and proceed through tutorials on a variety of natural language processing approaches to the treatment of text-as-data.

This course will provide an overview of machine learning (ML) with special attention to applications for social and behavioral analytics. Machine learning combines insights from artificial intelligence, probability theory, statistical inference, and information theory to help automate tasks involving pattern recognition, prediction, and classification. "Learning" is analogous to "inference" in statistics and, in fact, the modern statistical toolkit includes various machine learning methods developed to handle large (and messy) datasets.