Winter & Spring Online Classes

An introduction to Geographic Information Science (GIS) which is the science of spatial relationships, linking data to locations to explore relations between objects. Based in geographic thought and emerging from initial applications in natural resource management, GIS has evolved to be a universally applicable way of thinking and set of knowledge, skills, and practices. The goals of this course are to teach you basic GIS concepts through practice and theory, to enable you to make useful and meaningful contributions to various disciplines through spatial analysis. Throughout this course, you will be challenged to not only think spatially, but apply spatial analysis techniques within GIS.

Learn the basics of the R programming language while building essential data management and wrangling skills to meet the need for versatile data science expertise required for advanced quantitative coursework.

Learn the language of behavioral research with an emphasis on designing social science research. Engage with measurement reliability and efficacy, internal or external research design validity and generalizability.

Learn the causal thinking plus modules on measurement, prediction, exploratory data analysis, probability, and uncertainty (estimation, confidence intervals, hypothesis testing, power).

Python has gained immense popularity as a programming language due to its ability to handle diverse types of data, powerful libraries for data analysis, robust support for tasks such as web scraping and data extraction from online sources, and its widespread use in machine learning and deep learning communities. Python is known for its readability and ease of use, making it a favorite among beginners and seasoned programmers alike.

Learn how social scientists convey data through narrative and visual reports geared toward general audiences or non-scientists. This hands-on course provides students with the knowledge and skills needed to generate strong, data-driven communication.

You'll get 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.

Get ready for the job market through four units: (1) Identifying Your Talents; (2) Developing Your Professional Presence; (3) Polishing Your Professional Presence, and (4) Developing a Collaborative Mindset. Among other topics, there will be specific 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. There will be many opportunities for engagement and networking with alumni from the College of Social and Behavioral Sciences.  

Open to masters and PhD students only.  Students can contact the DACSS program ([email protected]) for more information.

Focus on advanced topics in survey design and analysis. Topics covered include different approaches to sampling, how to construct and use survey weights, and tools for analyzing and enriching survey data, including approaches to conducting matching and multiple imputation, as well as the construction and analysis of panel data. The course will also focus on designing and analyzing survey experiments.

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 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.

Learn 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? Do people form (or dissolve) social relations because of shared similarities (or outright differences) with others, or because they tend to be influenced by others already in their networks? Extending the discussion on descriptive and structural characteristics of network data in Social Network Analysis (DACSS 695N), this course introduces statistical frameworks with which network dynamics can be investigated.

You'll learn the relationship between law and social activism. The use of court decisions to effect change and mobilize support for causes. A critical look at such strategies to determine if and when they are effective in achieving activists' goals. How sociolegal scholars should define social change and understand the role legal professionals play in structuring movement practices. Activism may include the Civil Rights movement, the women's movement, environmentalism, and so-called "green backlash".

Pre-Requisite: Legal 250

This course is designed to help students improve their ability to analyze and write about complicated legal issues. You should expect to do a lot of writing in this course. You will learn how to read and understand court opinions and how to find your way around a law library. Writing assignments include your own resume and a job application letter, case briefs, memoranda, OP-ED essays, and a research paper. These assignments are written from the perspective of a lay person writing to another lay person. Satisfactory completion of this course fulfills your junior year writing requirement for the Legal Studies Department.

Open only to Legal Studies majors who have completed LEGAL 250 and College Writing (CW).