Winter & Spring Online Classes

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).

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. This introductory course on Python for data science will focus on the essential tools that are particularly beneficial for social data scientists and data professionals. This course will provide you with a solid foundation in Python and equip you with the necessary skills to effectively work with data using Python.

This course reviews different computational social science methods that are applied under different academic and professional situations. This includes different but complementary methods to format and explore data as tables, maps, graphs, and text. The course also includes basic methods for decision-making support such as optimization and social simulation. The course includes the use of version control applications, as well as promoting the practice of reproducibility and transparency. The course makes use of Python and R, including NetLogo for social simulation, Gephi for network exploration, and Git for version control. Those languages are not a pre-requisite for the course, as they will be used as templates.

Sports analytics has become an essential tool across professional and collegiate athletics, shaping how teams evaluate players, develop strategy, and make organizational decisions. This course introduces students to analytics in a wide range of sports, including the NFL, Major League Baseball, the NBA/WNBA, and soccer. Students will explore how data informs individual and team performance evaluation, in-game strategy, and front office decision-making.
The course blends traditional statistical approaches—such as correlation, regression, and other conventional methods—with more advanced techniques, including machine learning and generalized additive models (GAMs). By applying these methods to real-world sports data, students will gain hands-on experience in building, interpreting, and communicating analytical models that drive decisions on and off the field.
Each week we will cover a specific sport beginning with a lecture on key topics/research questions for the sport followed by guided R work to conduct analyses and draw conclusions.

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.

This course is intended as a math "boot camp" for incoming DACSS students and PhD students in certain social and behavioral sciences. Students will develop or refresh math skills needed for effectively learning applied statistics and computational methods. Topics covered include essential algebra review (basic skills, functions, exponents and logarithms, trigonometric functions); key concepts from calculus and their applied use; probabilistic reasoning, calculation, and distribution functions; and fundamentals of matrix arithmetic and linear algebra. Students who have taken college courses in calculus, probability, and linear algebra should not take this class, which is a crash course/review of fundamentals.

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.

• How do networks 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?

Apprise statistical frameworks with which network dynamics can be investigated. Extending the discussion on descriptive and structural characteristics of network data in Social Network Analysis (DACSS 695N).

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)

The purpose of this integrative experience course is to explore the relationship between law and social activism. To do this, we will examine how social movements and interest groups use the legal system to influence change and mobilize support for their causes. Because the study of social movements implicates a diverse array of perspectives, we will interrogate this topic from an interdisciplinary lens. We will devote special attention to issues of social justice; group formation and maintenance; how groups use the legal system in an attempt to influence public policy; legal strategies; the effectiveness and limits of litigation; and the role played by lawyers in using the courts to pursue social change. We will investigate these topics in the context of social movements related to civil rights, LGBT rights, women’s rights, the Second Amendment, and others. This course will provide you with a new way of thinking about the law, based on how a wide range of social science disciplines approach the study of law and social activism. In addition, you will learn how to understand and critique legal studies research, and hone your writing skills through the development of your own research papers.

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)

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.

Are you interested in developing your understanding and views on a broad range of public policy issues, and listening to those of others in an open, respectful, and inclusive classroom? Whether you know nothing about public policy or whether you have well-developed ideas, we'll have substantive and civil discussions about contemporary issues that affect us all, including immigration, trade and tariffs, international human rights, artificial intelligence, the environment, and many more. You'll also get to choose a topic that we'll discuss. The point is not just for you to learn what other people think but also to better understand, think for yourself, and articulate persuasively your own views about the policies your governments produce. There are no exams, and every assignment will help you develop professional skills useful to whatever you pursue after college. All course materials will be provided free to students.

This course will cover the fundamentals of data analysis and modeling in Excel. It will focus on generalized tools and approaches applicable to a wide range of applications rather than specific modeling or statistical analysis techniques. The course is intended for students with no prior Excel experience and will start with the most basic capabilities of Excel. However, it will work up to more advanced topics to provide students with existing Excel experience familiarity with Excel's more advanced features. Throughout, the course will emphasize the effective use of formulas and spreadsheet structure to streamline analysis approaches.

Financial management is one of the basic functions of all public and nonprofit organizations. Competency in financial management precedes competency in management. Without good financial management, even the best-run organization will not survive. As such, the set of skills and competencies required to manage financial resources effectively are some of the most important that you will learn during your graduate studies. This course will strengthen and develop the following knowledge, skills and abilities: Knowledge of the basics of financial accounting; Financial statement analysis skills; Financial condition analysis skills; Cost accounting skills; Knowledge of control functions (e.g., purchasing control, inventory control); Knowledge and application of the time value of money; Knowledge and application of appropriate software packages for financial information management; Knowledge of cash management techniques and instruments; Municipal bond markets and financial intermediation; Investment analysis techniques; Investment management.

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.