Data Science
Data are an omnipresent aspect of modern life. Commercial, governmental, and non-profit organizations increasingly depend on data for their daily operations and planning. Massive amounts of personal data are generated daily. How such data are used and interpreted raises significant moral and social issues and is likely to influence the well-being and functioning of individuals, communities, environments and societies.
The Data Science (DS) minor is an interdisciplinary program with courses in a number of departments. The DS minor provides an opportunity for students to learn about data analytics, computational approaches, data-driven decision making, data structures and management, and the social and ethical implications of data.
Students can complete the minor by selecting from a broad range of courses. The Data Science minor is intended to offer pathways for students from all divisions of the college. Students may complete at Data Science minor as a complement to any major in the TRICO.
Minor Requirements
The minor comprises six courses.
One course in each of two foundational areas:
| Code | Title | Units |
|---|---|---|
| Data Analytic Approaches | ||
| Select one of the following: | 1 | |
| Biostatistics with R | ||
| Introduction to GIS for Social and Environmental Analysis | ||
| Quantitative Methods for Urban Research | ||
| Introduction to Data Structures (or CMSC H106) | ||
| Data Analytics & Modeling for Non-Programmers | ||
| Select Topics in Mathematics | ||
| Theory of Probability with Applications | ||
| Research Methods and Statistics (or PSYC H200) | ||
| Intro to Research Design and Data Analysis for PoliSci (or POLS H277) | ||
| Quantitative Methods | ||
| Fundamentals of Computing | ||
| Select one of the following: | 1 | |
| Introduction to Data Science | ||
| Python for Data Science | ||
| Introduction to Computing (or CMSC H105) | ||
BIOL B115 | ||
| Additional Courses | ||
| Select four of the following: | 4 | |
| Introduction to Data Science | ||
| Data Ethics: Surveillance and Manipulation | ||
| Quantifying Happiness: Efforts to study and alter happiness | ||
| Advanced Data Science:Regression & Multivariate Statistics | ||
| Bayesian and Frequentist Statistical Inference | ||
| Biostatistics with R | ||
| Genomics | ||
| Computational Methods in the Sciences | ||
| Ecological Modeling | ||
| Introduction to GIS for Social and Environmental Analysis | ||
| Quantitative Methods for Urban Research | ||
| Topics in Advanced GIS | ||
| Introduction to Computing | ||
| Computer Science I | ||
| Introduction to Data Structures | ||
| Recent Advances in Computer Science | ||
| Introduction to Econometrics | ||
| Econometrics | ||
| The Science of Climate Change | ||
| Cataloging Collections | ||
| Praxis III - Independent Study | ||
| Survey Methods for Health Research | ||
| Basic Probability and Statistics | ||
| Select Topics in Mathematics | ||
| Introduction to Modeling and Simulation | ||
| Data Ethics in Social Media | ||
| Intro to Research Design and Data Analysis for PoliSci | ||
| Big Data, Big Impact, Big Responsibilities: Fundamentals and Ethics of Data Science | ||
| Research Methods and Statistics | ||
| Data Science with R | ||
| Reproducible Research in Psychology | ||
| Quantitative Methods | ||
| Capital & Connections:A Network Approach to Social Structure | ||
| Total Units | 6 | |
Additional Minor Guidelines/Requirements:
- Students can only count a total of 2 courses that they are using for major credit (or another minor) towards the minor
- At least two of the additional four courses beyond the two foundational requirements must be at the 200 level or above
DSCI B100 Introduction to Data Science (1 Unit)
“Data science” is a catch-all term used to describe the practice of working with and analyzing messy data sources to draw meaningful conclusions. This course provides a broad introduction to the field of data science via the statistical programming language.Over the semester, students will learn how to manipulate, manage, summarize and visualize large data sets. No previous exposure to programming or statistics is expected.
DSCI B103 Python for Data Science (1 Unit)
This course is an introduction to programming in Python for Data Science Students. It introduces basic programming skills in Python, and the most common data and file formats for acquiring, wrangling, and storing data. It introduces the essential Python libraries for Data Science: Numpy (useful for storing data in arrays, doing descriptive statistics, and numerical analyses fof data); Pandas (a useful library for data analysis); and Matplotlib (a library for plotting and visualizing data). The use of these libraries will be illustrated with examples of data exploration, analysis, and visualization in several contexts. This course will be considered equivalent to the courses listed in Fundamentals of Computing bucket for the Data Science Minor.
DSCI B201 Data Ethics: Surveillance and Manipulation (1 Unit)
Data ethics has become an increasingly important topic with the rise of big data and artificial intelligence. We are now tracked online and off, and our data is packaged and sold to the highest bidder. In this course we will ask: Why is privacy valuable, and how might it be protected in the age of big data? What is surveillance capitalism, and what is new about it? Are we being manipulated by algorithms that know too much about us? If so, what is the ethical harm of manipulation? Course materials will be drawn from diverse sources, and assignments will apply ethical theories to current problems. Students who took PHIL-B258: Data Ethics in Social Media in Spring 2024 should contact the instructor for permission to take this course.
DSCI B210 Quantifying Happiness: Efforts to study and alter happiness (1 Unit)
This course is designed to introduce students to the scientific study of happiness and psychological well-being. We begin with readings that will allow us to critically consider what is meant by happiness and well-being and then move on to evaluating approaches to measuring these constructs. We will examine studies that have tracked happiness and attempted to identify contributors to happiness. We will also look at efforts to increase happiness. We will ponder the ways in which culture and historical factors influence the study of happiness. Students will work directly with data sets measuring aspects of happiness. Part of the class meeting time will be used to support study work with data. Prerequisite: Intro to Data Science or a statistics class; coursework in the social sciences recommended but not required; Quantitative Readiness Required (QR)
DSCI B211 Data Analytics & Modeling for Non-Programmers (1 Unit)
This course covers several data analysis and modeling techniques: regression, classification, clustering, and time series analysis. The course will be taught using the Data Science Learning Platform (DSLP) developed explicitly for those who do not have a background in computer programming. Students will get hands-on experience with using existing and their own datasets to do analysis and modeling. This course fulfills the Data Analytic Approaches requirement for the Minor in data Science. Course prerequisites: Any course listed in the Fundamentals of Computing requirement for the Minor in Data Science: DSCI B100, Introduction to Data Science; CMSC B110. Introduction to Computing; CMSC B113, Computer Science I; or BIOL 115, Computing Through Biology.
DSCI B215 Power, Pluralism, and Parity: Intersectional Data Feminism (1 Unit)
Data is often perceived as objective and impartial, but the processes of data collection, analysis, and visualization are deeply influenced by existing power structures. In this course, we will explore the intersection of data and these power structures through the lens of feminist theory. Examining the intersection of data with gender, race, class, and disability, we will question how data can both reinforce and challenge systems of oppression. Readings and discussions will center the experiences of the affected communities as we explore methods to make data practices more inclusive and equitable. Through the combination of readings, class discussions, and hands-on activities, students will engage with key concepts in data feminism. We will apply these concepts to real-world examples, using Python for data analysis. No prior programming experience is required, as the course will provide the necessary foundational skills. By the end of the course, students will be equipped to critically analyze data practices and contribute to more just and ethical data-driven decision-making.
DSCI B310 Data in Action: Non-Profits and Data (1 Unit)
Non-profits experience similar pushes and pulls toward data-driven decision making as for-profit companies do. Funding organizations and donors expect quantitative analyses of program impact and success. The digital tools that organizations use to organize clients, volunteers and donors produce data that might be analyzed. However, overshadowing both trends is a very long history of public-sphere data collection and data-driven policy-making that disenfranchised and objectified the very communities that public and charitable institutions were ostensibly helping. What might it take to reconcile data collection and data-driven decision-making with more inclusive understandings of social justice? In this course you will work and learn with staff from local non-profit organizations as they develop practices for working and making decisions with data that align with their organizational values and goals. Prerequisite: At least two courses that count towards the data science minor
DSCI B314 Advanced Data Science:Regression & Multivariate Statistics (1 Unit)
This course is designed to improve your data science skills by introducing you to advanced statistical techniques that have become increasingly important in psychology and a variety of fields. The focus will be on understanding the advantages and limitations of regression approaches and multivariate analytic techniques that permit simultaneous prediction of multiple outcomes. Topics covered will include basic regression approaches, advanced regression strategies, structural equation modeling, factor analysis, measurement models, path modeling, modeling of longitudinal data sets, multilevel modeling approaches and growth curve modeling. Students will gain familiarity with these techniques by working with actual data sets. The last part of each class will be reserved for lab time to apply lessons from class to an assignment due the following week. Students are welcome to stay beyond the noon ending time to complete the assignment. Prerequisites: Required: PSYC Research Methods and Statistics 205 (BMC), Psych 200 (HC) Experimental Methods and Statistics, or BIOL B215 Experimental Design and Statistics. Students with good statistical preparation in math or other disciplines and some knowledge of core methods used in social science or health-related research should consult with the instructor to gain permission to take the class.This course was formerly numbered PSYC B314; students who previously completed PSYC B314 may not repeat this course.
DSCI B315 Bayesian and Frequentist Statistical Inference (1 Unit)
What are the different ways in which we can derive conclusions (and certainty of those conclusions) from the same sample of data? This course provides an introduction to the logic and application of statistical methods for analyzing data relevant to fields in data science utilizing two popular perspectives: the traditional Null Hypothesis Significance Testing (NHST) or Frequentist approach as well as the more contemporary approach of Bayesian inference. In doing so, we will tackle two of the most predominate ways of drawing conclusions about the world and gain important insight into quantifying uncertainty in our conclusions. Topics covered include data management and screening; methods for describing and presenting data; t-tests; analysis of variance; advanced applications of the general linear model (i.e., regression) including moderator analyses; and generalized versions of the general linear model such as logistic regression. Some of these topics may be seen as a review from the NHST perspective; however we will jump straight into modeling these parameters using the more flexible general linear model. This is an applied course in statistics. Thus, the emphasis is not on learning math (i.e., doing statistical analyses by hand). Rather, the major objectives of this course are for you to gain a conceptual understanding of statistical inference from both Bayesian and NHST perspectives, learn how to implement statistical analyses using both approaches on a computer using R (a free, open-source program), interpret R output, and communicate the results of statistical analyses in clear and compelling language. No prior knowledge of the R statistical platform is required. Prerequisites: BIOL 215 Biostatistics with R, or PSYC 205 Research Methods and Statistics, or SOCL 265 Quantitative Methods or A comparable statistics course in the BICO (e.g., PSYC H200).
DSCI L100 Introduction to Data Science - LACOL Summer (0 Unit)
BIOL B215 Biostatistics with R (1 Unit)
An introductory course in statistical analysis focusing on biological data. This course is structured to develop students' understanding of statistics and probability and when to apply different quantitative methods. The lab component focuses on how to implement those methods using the R statistics environment. Topics include summary statistics, distributions, randomization, replication, and probability. The course is geared around problem sets, lab reports, and interactive learning. No prior experience with programming is required. Suggested Preparation: BIOL B110 or B111 is highly recommended. Students who have taken PSYC B205/H200 or SOCL B265 are not eligible to take this course.
BIOL B216 Genomics (1 Unit)
An introduction to the study of genomes and genomic data. This course will examine the history of this exciting field, the types of biological questions that can be answered using large biological data sets and complete genome sequences as well as the techniques and technologies that make such studies possible. Topics include genome organization and evolution, comparative genomics, and analysis of transcriptomes, with a focus on animal genomics and humans in particular. Prerequisite: One semester of BIOL 110. BIOL 201 highly recommended.
BIOL B250 Computational Methods in the Sciences (1 Unit)
A study of how and why modern computation methods are used in scientific inquiry. Students will learn basic principles of analyzing, modeling, and visualizing scientific data through hands-on programming exercises. Content will draw on examples from across the life sciences. This course will use the Python programming language. No prior programming experience is required. Six hours of combined lecture/lab per week.
BIOL B312 Biodiversity Conservation in a Changing World (1 Unit)
How is biodiversity responding to human-induced global change, and how can ecological knowledge be applied to conservation? This course explores pressing ecological challenges such as endangered species management, invasive species spread, and climate-driven range shifts. Through case studies and primary literature, we will examine how conservation biologists, invasion ecologists, and population biologists use data-driven approaches to understand ecological dynamics, inform decision-making, and propose real-world solutions. Students will explore quantitative methods by analyzing and discussing scientific papers, gaining insight into the role of data and models in conservation science. By the end of the course, students will design a research proposal detailing how to collect and analyze data for forecasting ecological change or modeling biodiversity threats. Prerequisites: Any 200-level BIOL course OR ENVS 201 OR permission of instructor. Students should have completed at least one QM course.
BIOL B330 Ecological Modeling (1 Unit)
Unraveling the complexity of ecological systems calls for increasingly sophisticated quantitative approaches. Statistical models and simulations built on empirical data offer the means of exploring complex ecological questions to better understand ecological processes and inform environmental decisions. This class will introduce students to a variety of ecological models while instilling an appreciation for the strengths and limitations of each modeling technique, vital to characterizing inferences made from them. The course will be taught as a hands-on integrated lab/lecture, and students will be expected to program regularly, primarily in R. Prerequisite: BIOL B215 or BIOL B250.
CITY B201 Introduction to GIS for Social and Environmental Analysis (1 Unit)
This course is designed to introduce the foundations of GIS with emphasis on applications for social and environmental analysis. It deals with basic principles of GIS and its use in spatial analysis and information management. Ultimately, students will design and carry out research projects on topics of their own choosing. Prerequisite: At least sophomore standing and Quantitative Readiness are required (i.e.the quantitative readiness assessment or Quan B001).
CITY B217 Quantitative Methods for Urban Research (1 Unit)
This course offers a hands-on introduction to quantitative research methods for urban planning and policy analysis. Students will work on a real-world research project to develop the practical skills needed to design, conduct, and analyze original research at the complexity of a thesis-length project. The course teaches research design (crafting strong research questions and selecting appropriate methods), quantitative research methods (survey design and collection and analysis of secondary socio-spatial data), and data analysis (basic descriptive and inferential statistical analysis using Excel and SPSS). Additionally, students will engage with both the philosophical foundations and real-world best practices of ethical research.
CITY B328 Topics in Advanced GIS (1 Unit)
An advanced course for students with prior GIS experience involving individual projects and collaboration with faculty. Completion of GIS (City 201)
CMSC B109 Introduction to Computing (1 Unit)
The course is an introduction to computing: how we can describe and solve problems using a computer. Students will learn how to write algorithms, manipulate data, and design programs to make computers useful tools as well as mediums of creativity. Contemporary, diverse examples of computing in a modern context will be used, with particular focus on graphics and visual media. The Processing/Java programming language will be used in lectures, class examples and weekly programming projects, where students will learn and master fundamental computer programming principles. Students are required to register for the weekly lab. Prerequisites: Must pass either the Quantitative Readiness Assessment or the Quantitative Seminar (QUAN B001).
CMSC B113 Computer Science I (1 Unit)
This is an introduction to the discipline of computer science, suitable for those students with a mature quantitative ability. This fast-paced course covers the basics of computer programming, with an emphasis on program design, problem decomposition, and object-oriented programming in Java. Graduates of this course will be able to write small computer programs independently; examples include data processing for a data-based science course, small games, or estimating likelihood of probabilistic events, etc.. No computer programming experience is necessary or expected. Students are required to register for a weekly lab.
CMSC B151 Introduction to Data Structures (1 Unit)
Introduction to the fundamental algorithms and data structures using Java. Topics include: Object-Oriented programming, program design, fundamental data structures and complexity analysis. In particular, searching, sorting, the design and implementation of linked lists, stacks, queues, trees and hash maps and all corresponding complexity analysis. In addition, students will also become familiar with Java's built-in data structures and how to use them, and acquire competency using a debugger. Students must also register for the weekly lab. Prerequisites: CMSC B109 or CMSC B113 or CMSC H105, or permission of instructor.
ECON B253 Introduction to Econometrics (1 Unit)
An introduction to econometric terminology and reasoning. Topics include descriptive statistics, probability, and statistical inference. Particular emphasis is placed on regression analysis and on the use of data to address economic issues. The required computational techniques are developed as part of the course. Class cannot be taken if you have taken H203 or H204. Prerequisites: ECON B105 and a 200-level elective. ECON H201 does not count as an elective.
ECON B304 Econometrics (1 Unit)
The econometric theory presented in ECON 253 is further developed and its most important empirical applications are considered. Each student does an empirical research project using multiple regression and other statistical techniques. Prerequisites:ECON B253 or ECON H203 or ECON H204 and ECON B200 or ECON B202 and MATH B201 or permission of instructor.
GEOL B104 The Science of Climate Change (1 Unit)
A survey of the science behind climate change. Students will analyze climate data, read primary scientific literature, examine the drivers of climate change, and investigate the fundamental Earth processes that are affected. We will also examine deep-time climate change and the geologic proxies that Earth scientists use to understand climate change on many different time scales. This course is appropriate for students with little to no scientific background but is geared toward students who are considering a science major. Two 90-minute lectures per week.
GEOL B210 Cataloging Collections (1 Unit)
This course is an introduction to cataloguing as an integral component of museum collections management. Students will consider the history, theories, and practices of cataloguing as a museum practice as it relates to the different objectives of various types of museums (art, natural history, science, history, zoological). Students will explore how cultural attitudes, institutional policies, and social expectations have historically influenced, and continue to shape, the development of collections management policies and procedures, while undertaking projects related to collections research and cataloguing. They will evaluate and recommend standardized vocabularies to build a collections database that accommodates more complex histories while optimizing searchability. They will engage with instructors who are actively involved in the professional operations of and calls to “decolonize” collections, becoming trained in the fundamentals of cataloguing collections as they actively rethink these structures and contribute to object records.
GNST B425 Praxis III - Independent Study (1 Unit)
HLTH B302 Survey Methods for Health Research (1 Unit)
Surveys are widely used to measure the population prevalence of various health conditions; to better understand the scope and impact of exposure to social and economic stressors on population health; to monitor health-related knowledge, attitudes and practices; and to inform health systems strengthening efforts. Through course material and hands-on experience, students will master the basic elements of survey design, including, operationalizing constructs and formulating research questions, choosing a mode of survey implementation, pretesting the survey instrument, designing a sampling plan, managing field operations, and analyzing and interpreting survey data. Prerequisites: Completion of a 200-level course in the social sciences or permission of the instructor.
MATH B104 Basic Probability and Statistics (1 Unit)
This course introduces key concepts in descriptive and inferential statistics. Topics include summary statistics, graphical displays, correlation, regression, probability, the Law of Large Numbers, expected value, standard error, the Central Limit Theorem, hypothesis testing, sampling procedures, bias, and the use of statistical software.
MATH B195 Select Topics in Mathematics (1 Unit)
This is a topics course. Course content varies.
MATH B205 Theory of Probability with Applications (1 Unit)
The course analyzes repeatable experiments in which short-term outcomes are uncertain, but long-run behavior is predictable. Topics include: random variables, discrete distributions, continuous densities, conditional probability, expected value, variance, the Law of Large Numbers, and the Central Limit Theorem. Prerequisite: Math 201.
MATH B208 Introduction to Modeling and Simulation (1 Unit)
Mathematical models are constructed to describe the complex world within and around us. Computational methods are employed to visualize and solve these models. In this course, we focus on developing mathematical models to describe real-world phenomena, while using computer simulations to examine prescribed and/or random behavior of various systems. The course includes an introduction to programming (in R or Matlab/Octave), and mathematical topics may include discrete dynamical systems, model fitting using least squares, elementary stochastic processes, and linear models (regression, optimization, linear programming). Applications to economics, biology, chemistry, and physics will be explored. Prior programming experience not required. Prerequisite: MATH B102 or the equivalent (merit score on the AP Calculus BC Exam or placement).
PHIL B258 Data Ethics in Social Media (1 Unit)
From sharing our life experiences to reading the news, social media permeates our daily lives. It affects how we communicate, what we buy, and who we vote for. It also generates an immense amount of data, which is eagerly collected by individuals, corporations, and governments. In this course we will investigate some of the threats (and promises) of this data. We will ask questions like: What is the value of privacy online, and how might it be protected? Are we being manipulated by algorithms? Are the algorithms that generate and moderate content biased? What are some of the ways online data can be used for good? Students will investigate these questions through practical and theoretical approaches. Course materials will be drawn from diverse sources including philosophy, data science, sociology, legal theory, and the Internet. Visiting speakers will enrich our discussion by offering academic and professional perspectives on the uses and misuses of data.
POLS B233 Intro to Research Design and Data Analysis for PoliSci (1 Unit)
This course offers students an introduction to the research design and methods used in political science. Topics are as follows (but are not limited to): (1) Positivism vs. interpretivism, (2) Causal vs. descriptive inference (3) Conceptualization, operationalization and measurement, (4) Experimental design, (5) Quasi-experimental design, (6) Survey research and sampling, (7) In-depth interviewing, (8) Quantitative data analysis and statistics, (9) Case selection, and (10) Multi-method research design. Students will have problem sets to finish every two weeks for which they will use the necessary software (usually R and R Studio). At the end of the semester, they will submit a research design which they can use as a basis for their senior thesis.
POLS B345 Big Data, Big Impact, Big Responsibilities: Fundamentals and Ethics of Data Science (1 Unit)
The era of “big data” has dramatically altered the way people tackle political, social, and economic issues to analyze and generate solutions, as well as the way they conduct social science research. Data is powerful and beautiful, yet deceitful. As such, big data can create many impactful solutions across the world while carrying big risks that require bigger responsibilities. This course aims to help students also nurture an informed mindset of how to use data properly and to what end – from ethical, legal, and public policy perspectives. Prerequisite: One course in Data Science AND one course in Social Sciences or International Studies.
PSYC B205 Research Methods and Statistics (1 Unit)
An introduction to research design, general research methodology, and the analysis and interpretation of data. Emphasis will be placed on issues involved with conducting psychological research. Topics include descriptive and inferential statistics, research design and validity, analysis of variance, and correlation and regression. Each statistical method will also be executed using computers. Lecture three hours, laboratory 90 minutes a week.
PSYC B265 Computational Neuroscience (1 Unit)
This course introduces students to the field of computational neuroscience. Computational neuroscience uses mathematical models to understand the information carried in the brain at many scales: a single neuron, synaptic connections between neurons, and populations of neurons. Mathematical models help us gain a precise understanding of the dynamics of our nervous system and make better predictions by running simulations of the system. In this course, students will learn key concepts and topics in computational neuroscience. Topics include neural encoding and decoding, artificial neural networks, reinforcement learning, and Bayesian probability theories. They will gain hands-on experience formulating the mental processes in the brain in terms of mathematical equations and writing computer codes in programming languages such as Python and MATLAB to simulate these processes. Prerequisites: Introductory Psychology (PSYC B101 or PSYC H100), or Introduction to Neuroscience (NEUR H100).
PSYC B318 Data Science with R (1 Unit)
In this course, students will build and practice data science skills to tidy up disorganized real-world data sets, generate eye-catching visualizations, and craft easy-to-interpret, polished end-products in the R programming environment. Topics include experimental design, building statistical models, and visualizing uncertainty. Students will work throughout the term on an independent data science project leveraging real-world data to investigate their hypotheses culminating in a data blitz presentation. Students will learn how to respond to coding challenges with a puzzle-solving, growth-oriented mindset. No prior R experience is not required. Prerequsites: Required PSYC B205 (Bryn Mawr - Research Methods and Statistics), OR PSYC H200 (Haverford - Research Methods and Statistics), OR SOCLB265 (Bryn Mawr - Quantitative Methods).
PSYC B330 Reproducible Research in Psychology (1 Unit)
How do we know what we know and what we don’t know in empirical science? Can we trust the peer review process to filter out invalid claims and identify the claims with enough evidentiary support to merit inclusion in The Literature? This course has two primary aims. The first is to introduce students to the recent history and major conclusions of the “Open Science” reform movement in psychology and related sciences. Students will learn about the structural and methodological factors that are potentially responsible for the high proportion of false positive findings in psychology. The second aim is to introduce modern best practices in research design and statistical computing, which prioritize error control, transparency, and reproducibility. The course will provide a very gentle introduction to the R programming language, which students will use to produce a simple but fully reproducible statistical analysis in the format of a scientific report. Prerequisites: PSYC B205 or PSYC H200 or similar introduction to Research Methods and Statistics.
SOCL B265 Quantitative Methods (1 Unit)
An introduction to the conduct of empirical, especially quantitative, social science inquiry. In consultation with the instructor, students may select research problems to which they apply the research procedures and statistical techniques introduced during the course. Using SPSS, a statistical computer package, students learn techniques such as cross-tabular analysis, ANOVA, and multiple regression. Required of Bryn Mawr Sociology majors and minors. Non-sociology majors and minors with permission of instructor.
SOCL B327 Capital & Connections:A Network Approach to Social Structure (1 Unit)
Is it better to have a tightly knit circle of friends or several compartmentalized groups? And better for what--social support, academic achievement, finding a job, coming up with a new idea, sparking a social movement? How might we study questions like these? In this course, we will explore the various ways of understanding social connections as a resource--as a form of capital--and we will learn how to collect and analyze data about networks to investigate the structure of social networks. In particular, we will learn how to think about advantages and disadvantages as resulting from the structure and composition of our social networks. Prerequisite: At least one social science course or permission of instructor.