The courses below are examples count toward the substantive requirement for the MS in Data Analytics and Computational Social Science and are open to DACSS students only.
DACSS 631: Digital Behavioral Data
Algorithms and data increasingly power our private and civic life. Companies, nonprofits, and governments have invested heavily in data mining - the bulk collection of user behavior data from web platforms to understand public opinion and to forecast trends. A lot of fashionable terms, such as artificial intelligence and big data, are being thrown around these days. The public and regulators also become increasingly wary of the dark side of algorithms, the skepticism has culminated after the Cambridge Analytica scandal, and the revelation of alleged foreign propaganda in the US through social media. This course gives a practical understanding of how data mining and algorithms work. You can obtain (1) marketable computational skills in data analytics and visualization and (2) evidence-based critical perspectives on the algorithmic society we live in. This course is offered as part of the new graduate program in Data Analytics and Computational Social Science, and would count towards the technical electives course distribution requirement of the master's degree.
DACSS 632: News and Public Opinion
Digital technology has transformed the news environment and diversified how public opinion is formed and expressed. This course is designed to offer a framework for understanding the broad impact of these changes on citizens as well as the democratic system. We will discuss foundational theories of political communication and address emerging issues in the digital news environment, ranging from the use of artificial intelligence in the newsroom to the changing perceptions of information credibility.
DACSS 633: Social Influence and Persuasion
Social influence is a powerful driving force for human behavior. Understanding the fundamental mechanism of social influence is essential for strategic communication and decision-making, whether you are in data science, management, or communication. In this course, we will learn why and under what conditions our attitudes and actions can be influenced by those around us. We will explore key theories and research findings in social psychology and economics, and apply persuasive communication techniques through case studies and hands-on projects.
DACSS 690M: Mathematics for Applied Data Science
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 statistics and computational methods. Topics covered include essential algebra review (basic skills, functions, exponents and logarithms, trigonometric functions), differential calculus concepts (for optimization, as in estimation theory), integral calculus (for calculating probabilities), matrix algebra, vector spaces, eigenvalues (e.g. for statistical theory, dimension reduction), and probability functions and calculations. (Designed to prepare students for data science coursework, this class will not itself be counted as a technical elective in the M.S. degree.)
DACSS 690S: Social Media Analysis
In this course, we delve into the techniques of data collection and mechanisms for analysis that are revolutionizing the fields of social media analysis and computational social science. Unlike traditional studies, our focus is on the vast and dynamic data sets generated by social media, ranging from mental health trends to income inequality and from viral fads to social unrest dynamics. To decode this "Big Data," we employ sophisticated computational methods, shedding light on its unique challenges, such as validity and representativeness. Our exploration doesn't stop at social media; we extend into the broader realm of social phenomena joining traditional data from governmental and private sources. We'll dissect real-world case studies that leverage state-of-the-art computational techniques like text analysis through Natural Language Processing (NLP), network analysis, latent-variable statistical models, and agent-based simulations.
DACSS 691P: Polishing Your Professional Presence
The course is designed to prepare students 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. Students can contact the DACSS program (@email) for more information.
DACSS 695BD: Machine Bias and the Law
In this course, we will explore the political, legal, and social implications of Big Data, artificial intelligence, and the increasing reliance on automated, machine learning algorithms across many different decision-making contexts, with a particular focus on the manifestations of bias in computationally or machine-based decisions. The course will address both the promise and perils of data-based, machine decision-making as well as the multiplicity of ways in which it already influences our daily lives. Topics will include predictive policing, privacy concerns on social media and in consumer choice, Facebook news recommendation and free speech, the transformation of the legal profession, racial and gender discrimination in language, and health care policy.
DACSS 695SL: Social Life of Algorithms
Algorithmic systems are at the center of today's digital world, and mediate communication processes in areas as diverse as social media, journalism, healthcare, and governments. How do algorithmic systems capture, represent, and transmit information about everyday interactions? How do they shape, and are shaped by, social, cultural, and political life? What kind of new issues and concerns arise from their ubiquitous use? This course provides a critical introduction to algorithmic systems, and how they relate to issues of communication, power and inequalities in society. In addition to reading responses and a midterm essay, students will complete a research project on an algorithmic system of their choice to unpack how they are constructed and used in everyday life.
SPP 603: Public Policy Analysis
Integrates material from core courses and applies it to actual and hypothetical policy issues in many areas. Examines policy analysis methods using case studies from a wide range of substantive policy areas. Looks at social, economic, organizational, political, and other influences on policy decisions.
SPP 605: Economics and Public Policy
Introduction to microeconomics theory and policy analysis. Examines economic rationales for and against government policy and the economic consequences of public policy.
SPP 690STM: Performance Management
This course will focus on the fundamentals for designing and using a performance-based management framework in the public sector, specifically in the U.S. at the federal, state, local, and nonprofit levels. It will provide a working understanding of how to develop and apply useful measures that are used a simple statement that is devilishly difficult to actually do, but is fundamental for every public manager to be successful.
SPP 690STN: Using the Past to Create Effective Policy
This class is a methods class, but we will have a central thematic thread: urban policy as racial conquest. This means that we will be focusing on the ways in which urban policy and the management of cites has worked as a form of racialized governance: restricting where certain people live, how they move through the city, what they are allowed to do, redistributing their wealth, limiting their political capital, and differentially burdening them with pollution while denying them of infrastructural benefits. This class will not be particularly uplifting, but it is my firm belief that we must seriously consider the ways that policy has been intentionally used to harm people if we are to ever be able to successfully use it to help. By the end of this class you will: 1. Develop a set of practical skills relating to data collection including: handling and digitizing documents, getting access to an archive (including relating to archivists, legal issues, online archives, etc.). 2. Develop a set of practical and conceptual skills relating to data analysis including: open and closed coding, software usage, data integrity and reliability, triangulation etc. 3. Become familiar with the various epistemic contributions that comparative and historical work can make including: causality, conceptual interrogation, theory building and testing, policy analysis. 4. Practice conducting historical work in a policy environment including: issues with translation to stakeholders and how to make compelling policy arguments. 5. Give those students with more research-based inclination an opportunity to get practical help on already existing projects.
SPP 690STP: The Future of Government
This course invites students to discover, discuss, debate and reflect on the benefits and negative consequences of increasingly digitized governance. We will focus on strategy, management and operations which entail empathy and understanding of stakeholders, collaboration across boundaries, learning and adaptability, among other human and social dimensions of organizational life. We will typically work from the perspective of the advocate, leader, or public servant trying to get things done and to increase public value or the public good. The course challenges students to analyze actual case studies of innovation, promising practices and serious challenges that public servants and public managers face. Course topics cover a broad and emerging terrain including public engagement and participation, open government and open data, visualization and mapping, multistakeholder collaboration, cross-boundary networks, design thinking, agile development, smart cities and the future of work. The course and discussions are as concerned with managing change, preserving democracy, and reducing inequality as they are with introducing cutting edge ideas. In a domain chock full of hype and extravagant promises, you should leave the course better equipped to see through the hype, to evaluate the feasibility of several digital innovations, and to examine unintended consequences of digitization related to increasing inequalities and biases. Course materials include case studies, reports and other practical writing in addition to research articles. Students will be expected to co-produce materials for themselves and their peers in the class to deepen knowledge of their particular interests and to participate actively in building the learning community that will constitute our class.