Undergraduate Course Descriptions

This course serves as an entrance to the world of data science and is aimed at students who have little to no background in data science, statistics, or programming. The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modeling, and effective communication of results. This course, which will rely on R, the statistical programming language, will prepare students for more advanced data science and computational social science courses.

How do new ideas spread online? Why do some take off and others fail? What determines when people will cooperate and when they will be selfish? Where do our social norms come from, and what happens when they are disrupted – as they were during the first year of the pandemic? How did ‘wearing a face mask’ and ‘getting vaccinated’ become political issues and what role did social media play in this? Why is communicating about climate change so challenging? The last several decades in social science have seen remarkable breakthroughs in our answers to these and other profound questions about societal communication and evolution. One of the most powerful and influential tools behind these breakthroughs is computational modeling. Models are used to simulate the spread of COVID, and to test strategies for halting the pandemic. They are used to test strategies for international relations, and to predict the emergence of new terrorist cells. Models are also used to predict voting outcomes and create better forms of political representation. This class does not involve coding and no programming experience is necessary. Instead, students will be introduced to a range of computation models and learn how they work to guide our governments and businesses. You will learn about the big ideas and simple formulas that are used to predict the future of our economy, our society, and our ecology.

Acquiring and demonstrating data literacy, namely, the ability to find, appropriately handle, analyze, and communicate insights from the rapidly growing spectrum of data in all aspects of modern life, is now a vital skill for virtually all workers and researchers. This course provides a foundation in the concepts, methods, and applications of data science (including network science) to questions in Communication. The course will build data literacy and help you start to develop skills working with large and complex datasets of relevance to communication behaviors in the digital world. Students will become familiar with basic programming skills for data analysis using the R and Python programming languages, along with some of the common tools used for network and data analysis and visualization. It will provide an introduction and overview of the key elements of applied data science, including the analysis of networks and machine learning (ML). The practical and ethical challenges of 'big data' and the increasing use of algorithmic (ML) decision systems will also be explored. No prior programming or data analysis experience is required.

In this 'big data' era, presidents and popes tweet daily. Anyone can broadcast their thoughts and experiences through social media. Speeches, debates and events are recorded in online text archives. The resulting explosion of available textual data means that journalists and marketers summarize ideas and events by visualizing the results of textual analysis (the ubiquitous 'word cloud' just scratches the surface of what is possible). Automated text analysis reveals similarities and differences between groups of people and ideological positions. In this hands-on course students will learn how to manage large textual datasets (e.g. Twitter, YouTube, news stories) to investigate research questions. They will work through a series of steps to collect, organize, analyze and present textual data by using automated tools toward a final project of relevant interest. The course will cover linguistic theory and techniques that can be applied to textual data (particularly from the fields of corpus linguistics and natural language processing). No prior programming experience is required. Through this course students will gain skills writing Python programs to handle large amounts of textual data and become familiar with one of the key techniques used by data scientists, which is currently one of the most in-demand jobs.

Today masses of data are available everywhere, capturing information on just about everything and anything. Related but distinct data streams about newsworthy events and issues -- including activity from social media and open data sources (e.g., The Open Government Initiative) -- have given rise to a new source for and style of reporting sometimes called Data Journalism. Increasingly, news sites and information portals present visually engaging, dynamic, and interactive stories linked to the underlying data (e.g., The Guardian DataBlog). This course offers an introduction to Python programming for data analysis and visualization. Students will learn how to collect, analyze, and present various forms of data. Because numbers and their visualizations do not speak for themselves but require context, interpretation, and narrative, students will practice making effective stories from data and presenting them in blogs and other formats. No programming experience is required for this class.

Communication technologies, including the internet, social media, and countless online applications create the infrastructure and interface through which many of our interactions take place today. This form of networked communication opens new questions about how we establish relationships, engage in public, build a sense of identity, promote social change, or delimit the private domain. The ubiquitous adoption of new technologies has also produced, as a byproduct, new ways of observing the world: many of our interactions now leave a digital trail that, if followed, can help us unravel the determinants and outcomes of human communication in unprecedented ways. This course will give you the theoretical tools to critically analyze the impact that networked technologies have on social life and inform your assessment of current controversies surrounding those technologies.

Digital technologies have made communication networks ubiquitous: even when we can't really notice them, they mediate most aspects of our daily activities. Networks, however, have always been the backbone of social life: long before Facebook, Twitter, Snapchat, or other similar platforms, communication created channels for information diffusion that linked people in myriad other ways. Through letters, commerce, or simply face to face interactions, people have always been exposed to the behavior of others. These communicative ties embed us into an invisible web of influence that we can make tangible and analyze. This course will teach you how to map those connections in the form of networks, and how to study those networks so that we can improve our understanding of social life. The goal is to help you grasp the consequences of connectivity, and how small changes in the structure of our ties can lead to big differences in how networks behave.

Digital information and communication technologies are intertwined with our everyday lives, from banking, to working, and dating. They’re also increasingly crucial parts of our most powerful institutions, from policing, to the welfare state, and education. This course examines the ways that these technologies combine with traditional axes of inequality like race, gender, and class in ways that may deepen social inequality. We’ll consider major approaches to understanding digital inequalities and apply them to case studies of both problems and solutions. Students will learn to critically analyze policies and programs from a variety of perspectives, and to evaluate the promise of digital technologies against their potential perils.

Algorithms regulate many areas of social life: they shape the information you see online, how resources are allocated, or how hiring and matching happen in private and public settings. In these and many other examples, algorithms rely on data informing the automated decisions they encode. Our ability to think critically about that data is, thus, paramount to understanding how the algorithms operate. In this course, we will discuss how data is transformed into information and actionable knowledge. You will learn how to question data to ensure their validity, reliability, and representativeness. Understanding how data are collected, analyzed, and used is key to being able to demand transparency in automated decision-making, and to exercising our democratic role of demanding accountability when decisions are made based on questionable data.

This course explores the nature of diffusion through social networks, the ways networks are formed and shaped by social structures, and the role they play in health behavior, public policy, and innovation adoption. Topics include: the theory of social networks; the small world model of network structure; constructing models to represent society; the social bases of the adoption of innovations and the spread of new ideas; the role of social networks in controlling changes in public opinion; the emergence of unexpected fashions, fads, and social movements; and the connection between social network models and the design of public policy interventions. Students will learn how to use the agent-based computational modeling tool "NetLogo", and they will work directly with the models to understand how to test scientific theories. We will examine the basic theory of social networks in offline, face-to-face, networks, as well as the role of online networks in spreading new ideas and behaviors through social media. Long standing debates on the effects of social networks on changing beliefs and behaviors, their impact on social change, and ethical concerns regarding their potential manipulation will be given careful consideration throughout. Students will be taught new skills that will enable them to use and develop their own agent-based models.