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 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.

This seminar series offers an exploration of the possibilities of data-driven innovation in the social sciences and other industries. It is intended to help participants think about looking at data differently - to spark different perspectives on ways to see, interpret, and use information. Guest speakers will include UMass alumni/ae and others representing a wide range of public- and private-sector and non-profit organizations in fields such as Politics, Health, Tech, Environment, Education and Social Services, Marketing and Communications, and Finance/Economics.

This course serves as a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master?s students with a data analytics or computational social science focus. The material covered will include a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power).

This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research. An emphasis is placed on measurement reliability and validity, internal research design validity, and generalizability, or external research design validity.

This course provides students with an introduction to the R programming language that will be used in all core courses and many of the technical electives. There is a growing demand for students with a background in generalist data science languages such as R, as opposed to more limited software such as Excel or statistics packages such as SPSS or Stata. The course will also provide students with a solid grounding in general data management and data wrangling skills that are required in all advanced quantitative and data analysis courses.