Winter & Spring Online Classes

This course explores foundational leadership concepts for nonprofit and public service
professionals through the lenses of trust, purpose, and ethics. Students will examine leadership as a moral and relational practice rather than a position of authority and will explore the grounding of leadership within the values, dilemmas, and responsibilities unique to public service. Students will develop a personal leadership toolkit, walking away with
an actionable framework of habits, values, and strategies for leading in complex, service-oriented
organizations.

Introduction to microeconomics theory and policy analysis. Examin economic rationales for and against government policy and the economic consequences of public policy.

Learn how technology can be a force for societal good when designed with public interest values at its core. It delves into the potential for technologies to promote equity, ethics, transparency, sustainability, justice, and public participation while recognizing the significant risks of neglecting these principles. Without deliberate attention to these values, technology can exacerbate inequality, amplify bias, undermine privacy, and consolidate power in ways that harm society.

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.

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. Students will become familiar with a range of techniques used to gather social science data and measure and analyze different aspects of individual and social behavior, including experiments, surveys, semi-structured interviews, focus groups, coding of online and archival text sources, and social network analysis. Students will learn to identify threats to research validity and reliability associated with these different research approaches. All data analysis will be conducted in R. Students will also use Qualtrics and mTurk to collect data. This course is a required core course for the graduate certificate and the master's degree in Data Analytics and Computational Social Science (DACSS).

GIS is the science of spatial relationships, linking data to locations to explore the interaction between objects. Originating in natural resource management, GIS has become a versatile framework of concepts, skills, and practices. Learn core GIS theory and methods to apply spatial thinking and analysis across disciplines.

How can social scientists convey data through narrative and reports geared toward general audiences or specific stakeholders? How can they convey those data through visuals geared toward non-scientists? Comprehend the knowledge and skills needed to generate strong, data-driven communication.

Prepare to enter the job market by:

• Identifying your talents
• Developing & polishing your professional presence
• Evolving a collaborative mindset

Attend workshops with trained professionals and alumni on writing CVs and cover letters, interviewing, creating an elevator pitch, identifying and making the most of personal strengths (using the Clifton Strengths Assessment), building a personal website, and more.

This course gives students the tools to show insights to political or scientific communities, while presenting different strategies to avoid biased interpretations. Given the overwhelming computational toolbox for displaying information, the course follows a "keep it simple" approach from the beginning, starting from foundational topics relating color, nature of data, and the brain; and takes students to build their own visualization tools. Emphasis is placed on complex data such as networks, geography and multivariate models. While the course uses R, it makes no emphasis on programming and more on the building of templates to produce information.

Grasp an overview of machine learning (ML) with special attention to applications for social and behavioral analytics. Machine learning combines insights from AI, probability theory, statistical inference, and information theory to help automate tasks involving pattern recognition, prediction, and classification.

First learn how to convert text to formats suitable for analysis. Then, get introduced and proceed through tutorials on a variety of natural language processing approaches to the treatment of text-as-data. This will include relatively simple dictionary approaches for measurement, supervised learning approaches for document classification, vector representations, contextualized embeddings, and more.

Delve into your 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.

Interdisciplinary exploration of basic issues of law's relationship to contemporary society, in which law affects almost all human activity. Topics include the nature as well as historical and social functions of law; the culture and role of major actors in the legal system (lawyers, judges, juries, police, technology); tension between ideals and realities in law; role of law in addressing contemporary social problems. This general education course, based on the assumption that "law is too important to be left to lawyers," is intended to foster analytical and critical skills and to discuss broad, interdisciplinary questions of individual responsibility, social morality, and justice. (Gen.Ed. SB, DU)

Explores the concept and practice of health as a human right, from an interdisciplinary perspective rooted in the social sciences. Despite strong evidence that social factors like poverty, inequality, racial and ethnic exclusion; we still tend to seek technological solutions to disease rather than examining ways to alter these underlying causes.