The DACSS program regularly offers eight core courses (required for MS in DACSS students) that provide students with a solid grounding in data collection, programming and data management, statistical data analysis, data visualization and communication, and effective evidence-based decision-making.
DACSS 600: Essential Math for Applied Data Science
This is a preparatory course that gives students sufficient background in probability, matrix algebra, and basic calculus concepts.
DACSS 601: Data Science Fundamentals
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 required in all advanced quantitative and data analysis courses.
This course is a required core course for the graduate certificate and the master’s degree in Data Analytics and Computational Social Science (DACSS).
DACSS 602: Research Design
This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research, including measurement reliability and validity, internal research design validity, and generalizability, or external research design validity. Students will become familiar with various techniques 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).
DACSS 603: Introduction to Quantitative Analysis
This course provides a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master’s students with a focus on data analytics or computational social science. The material covered includes a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power). Along the way, students will encounter linear regression and classification as tools of descriptive data summary, prediction and inference and as part of a broader strategy of causal analysis. Simulations and data analysis will be conducted in the R statistical environment.
This course is a required core course for the graduate certificate and the master’s degree in Data Analytics and Computational Social Science (DACSS).
DACSS 713: Advanced Statistical Methods or DACSS 756: Machine Learning for Social Sciences
DACSS 713 Advanced Statistical Methods
This course will build on students' 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 at the discretion of the instructor. These will include special cases of GLMs and multilevel models and may also consider measurement of latent variables (e.g. factor analysis, IRT).
DACSS 756 Machine Learning for Social Scientists
This course will provide an overview of machine learning (ML) with special attention to social and behavioral analytics applications. Machine learning combines insights from artificial intelligence, probability theory, statistical inference, and information theory to help automate tasks involving pattern recognition, prediction, and classification. "Learning" is analogous to "inference" in statistics, and the modern statistical toolkit includes various machine learning methods developed to handle large (and messy) datasets. The course focuses on statistical learning and is a good second or third course in statistical methods for graduate students in the social and behavioral sciences. We will examine key supervised and unsupervised learning techniques and reflect upon appropriate and inappropriate applications of such approaches for those seeking to understand the social world. We shall also discuss the ethical issues involved in automated analysis and computer-assisted decision-making, including how they may sometimes help overcome human biases and, in others, only reinforce these tendencies.
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. Open to DACSS M.S. students only.
DACSS 621: Ethics of Data Science & AI
Data science is rapidly changing the world. Algorithms and AI are increasingly utilized - often unknowingly - by citizens, elected officials, and institutions. As our decision-making processes and very sense of reality are ruptured, we are undoubtedly entering uncharted waters. These products and methodologies offer unparalleled ways to answer complex social questions, but they also provoke daunting ethical challenges for individuals and societies. Indeed, even with the best intentions, data science raises questions of transparency, equity, and fairness. In this seminar style class, we will explore these questions and investigate fascinating applications of data science like housing and bail algorithms - among others - as we seek a better understanding of our social obligations to one another. We will also grapple with how private and public entities utilize AI and data science broadly to serve our commercial and civic interests, forcing us to delve into questions of accountability, transparency, and efficiency.
DACSS 684 Adv Data-Driven Storytelling or DACSS 698R Research Lab
Students will have the option of DACSS 684: Advanced Data-Driven Storytelling, an online course that provides students with the knowledge and skills needed to generate strong, data-driven communication or DACSS 698R Practicum - Research Lab, an in-person collaborative course with possible client-based or team projects. Please note that DACSS 684 and DACSS 698R are only offered in fall and spring semesters.