Matt Lawrence

The Family in Contemporary Society
This course will investigate the social, economic, and political forces that have brought about changes in family life in the beginning of the 21st century. We will begin by looking at various attempts to define "the family," and we will then explore a range of topics, including the webs of family relationships (e.g., mothering, fathering, kin networks), labor and family intersections (e.g., mediating between work and family; the household division of labor), gay and lesbian family life, and domestic violence. Although the focus will be on contemporary United States, we will also examine some cross-cultural and historical material. 3 hrs. lect.

Social Life in an Age of Big Data
Until recently, quantitative social science relied on surveys or official statistics. Today, sociologists may link social media profiles to census records or student loan statements. In this course, we will consider some of the insights that such sources and methods of "Big Data" reveal about society. We will also critically examine the ethical dilemmas and cultural consequences of living in a world where many of our actions and interactions can be turned into data. Readings, discussions and occasional applied exercises will introduce students to core sociological topics and develop the tools to consume and engage quantitative social science research. (formerly SOAN 0245) 3 hrs. lect.

The Logic of Sociological Inquiry
In this course students will be introduced to the basic tools of sociological research including problem formulation, strategies of design and data collection, and analysis and presentation of results. This class will help students formulate a research question and develop a research strategy to best explore that question. Those strategies may include interviews, structured observation, participant observation, content analysis, and surveys. This class, strongly recommended for juniors, will culminate in the submission of a senior project proposal. (SOAN 0105 or SOCI 0105) (formerly SOAN 03010) 3 hrs. lect./disc.

Higher Education and Society
Concerns about quality, value, and cost have raised doubts about whether higher education remains a pathway to opportunity. In this course we will consider these issues by reviewing research on the changing demographics of students, the evolving definition of “merit” in admissions, the challenges of assessing what students learn, and the relationship between student loan debt and economic inequality. We will also examine how college shapes later outcomes such as income, health, and family formation. Finally, we will discuss efforts to reform higher education and the potential for innovations like global expansion to reshape postsecondary schooling in the future. 3 hrs.lct. (Formerly SOCI 0430. Not open to students who have already taken SOCI 0430.)

Topics in Sociological Practice
In this project-based course, we will use tools of data analysis and digital methods to explore sociological research topics. We will learn the fundamentals of coding and statistics in order to conduct original data analyses using publicly available data from sites such as the Opportunity Atlas, Social Explorer, the General Social Survey, and the Department of Education. We will reflect on how our computational and research practices relate to data ethics, principles of data visualization, and digital pedagogy. Students will build on their previous coursework to develop interactive exercises to analyze and interpret data which will be integrated throughout the department's curricula. Students will extensively use programs such as R, Shiny, and Datawrapper but no previous experience with these tools is required.

Counting the U.S.: The 2020 Census
In 2020 the government will count each resident of the United States as part of the constitutionally-mandated decennial census. In this course we will consider the importance and challenges of the census for measuring trends in sociological topics such as education, inequality, family composition, and race. Students will develop their skills in quantitative social science research by completing an independent project using census data. The class may also collaborate with local community organizations to use census data to identify how and where to provide services. Previous experience with the software package R is helpful, but not required.

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Sociology, Neuroscience, Animation, Art History, or Environmental Science. This course will utilize the R programming language. No prior experience with R is necessary.
ENVS: Students will engage in research within environmental health science—the study of reciprocal relationships between human health and the environment. High-quality data and the skills to make sense of these data are key to studying environmental health across diverse spatial scales, from individual cells through human populations. In this course, we will explore common types of data and analytical tools used to answer environmental health questions and inform policy.
FMMC: Students will explore how to make a series of consequential decisions about how to present data and how to make it clear, impactful, emotional or compelling. In this hands-on course we will use a wide range of new and old art making materials to craft artistic visual representations of data that educate, entertain, and persuade an audience with the fundamentals of data science as our starting point.
NSCI/MATH: Students will use the tools of data science to explore quantitative approaches to understanding and visualizing neural data. The types of neural data that we will study consists of electrical activity (voltage and/or spike trains) measured from individual neurons and can be used to understand how neurons respond to and process different stimuli (e.g., visual or auditory cues). Specifically, we will use this neural data from several regions of the brain to make predictions about neuron connectivity and information flow within and across brain regions.
SOCI: Students will use the tools of data science to examine how experiences in college are associated with social and economic mobility after college. Participants will combine sources of "big data" with survey research to produce visualizations and exploratory analyses that consider the importance of higher education for shaping life chances.
HARC: Students will use the tools of data science to create interactive visualizations of the Dutch textile trade in the early eighteenth century. These visualizations will enable users to make connections between global trade patterns and representations of textiles in paintings, prints, and drawings.