ARTG 5100 Information Design Studio 1 – Principles: Explores the theories and practices of information design through studio projects. Investigates visual systems and information structures such as maps, timelines, charts, and diagrams. Emphasizes the creative process of organizing, visualizing, and communicating data by seeking to make complex information easier to understand and use. Requires graduate standing or permission of the program coordinator or instructor.
ARTG 5120 Research Methods for Design: Examines qualitative and quantitative research methods pertinent to design. Through discussion and writing activities, offers students an opportunity to investigate varied inquiry toward the development of researchable questions, argument formation, and assessment methodologies. Students who do not meet course restrictions may seek the permission of the instructor.
ARTG 5150 Information Visualization Principles and Practices: Introduces information visualization from theoretical and practical perspectives. Defines the information visualization domain and advances principles and methods for the effective visual representation of data. Contextualizes the field from a historical perspective. Presents the perceptual and cognitive tasks enabled by visualizations. Studies an extensive range of visualization models. Illustrates good and bad practices in visualization with real-world examples. Introduces concepts in computer programming in an information visualization context.
CS 6120 Natural Language Processing: Provides an introduction to the computational modeling of human language, the ongoing effort to create computer programs that can communicate with people in natural language, and current applications of the natural language field, such as automated document classification, intelligent query processing, and information extraction. Topics include computational models of grammar and automatic parsing, statistical language models and the analysis of large text corpuses, natural language semantics and programs that understand language, models of discourse structure, and language use by intelligent agents. Course work includes formal and mathematical analysis of language models, and implementation of working programs that analyze and interpret natural language text.
CS 7250 Information Visualization – Theory and Applications: Covers foundational as well as contemporary topics of interest in data visualization to enable the effective representation of data across disciplines, including examples drawn from computer science, physical sciences, biomedical sciences, humanities, and economics. Topics include data visualization theory and methodology, visualization design and evaluation, visual perception and cognition, interaction principles, and data encoding and representation techniques.
CS 7260 Visualization for Network Science: Covers the principles of information visualization in the specific context of network science. Introduces visual encoding of data and our understanding of human vision and perception; interaction principles including filtering, pivoting, aggregation; and both quantitative and human subjects evaluation techniques. Covers visualization techniques for several network types, including multivariate networks with attributes for entities and relationships, evolving and dynamic networks that change over time, heterogeneous networks with multiple types of entities, and geospatial networks. Offers students an opportunity to learn about the design of layout algorithms for node-link and matrix visualizations.
CS 7290 Special Topics in Data Science: Offers special topics in data science, including machine learning, statistics, data mining, parallel and distributed data analysis, database systems, information retrieval, knowledge representation, information visualization, natural language processing, computational biology and bioinformatics, computational social science, digital humanities, health informatics, business, and predictive analytics.
ENGL 7370 Topics in Digital Humanities – Introduction to Digital Humanities: Offers a critical orientation to the tools, methods, and intellectual history of the digital humanities (DH). Explores key questions such as what debates are (re)shaping DH in this moment; what central theories lead humanities scholars to experiment with computational, geospatial, and network methodologies; how visualization can illuminate literature, history, writing, and other humanities subjects; and how new modes of research and publication might influence our teaching. Balances theory and praxis: Successful students come away with a well-grounded understanding of the DH field and a set of foundational skills to support their future research. No prior technical expertise is required to take the course, but students should be willing to experiment with new skills.
ENGL 7380 Topics in Digital Humanities – The Shape of Data in the Humanities: This course is intended as an introduction to the concepts and basic practices of data modeling for researchers in digital humanities. We will move from discussions of very simple data formats to more complex formats such as databases and XML, with hands-on explorations including Omeka and XML schema-writing. The readings provide critical context and help situate the theory and practice of data modeling within the domain of digital humanities research. Students will come away with a strongly grounded understanding of how humanities data is shaped and used, and how those practices affect critical and scholarly practice. No prior technical experience is assumed, although at times the course will move quickly; we will organize lab sessions and opportunities for extra help as needed.
ENGL 7380 Topics in Digital Humanities – Reading Machines: Technology and the Book: “Reading Machines” will pivot around the double valence of its title, outlining a literary history of new media from the hand-press period to the present. Our approach will draw on scholarship in book history, bibliography, media studies, and digital humanities, an intersection described by N. Katherine Hayles and Jessica Pressman as “comparative textual media.” We will take this comparative, interdisciplinary approach first to better understand machines of reading (e.g. the printed book, the internet) as material, historical, and cultural objects. We will examine how practices of reading, writing, and publishing have interacted—thematically and materially—with contemporaneous technological innovations over the past 250 years. We will complement our readings with praxis, gaining hands-on experience with textual technologies from letterpress (using the English Department’s new letterpress studio) to computer programming, as well as direct experience with archival materials in special collections around Boston. Together, weekly “book labs” and course discussions will help us consider relationships among modes of textual production, reception, and interpretation: including in our purview both “intellectual work,” such as writing, and “manual labor,” such as typesetting.
ENGL 7380 Topics in Digital Humanities – Queer Digital Curation: Developed at the intersection of theory and practice, this course will introduce students to queer theory in the context of digital curation. Digital curation refers to the selection, organization, preservation, and representation of digital resources; it is largely focused on efficiency, structure, and use. Queer theory, on the other hand, critically investigates cultural normativities related to sex, sexuality, and gender; it often values disruption, deconstruction, and play. A queer approach to digital curation, then, will allow us to unpack the invisible norms of digital environments as we think through the effects of digital tools, particularly those used for social justice purposes. There will be a hands-on unit of this course; however, prior knowledge of LGBTQ+ issues or the digital humanities is not expected––all students are welcome in this course.
HIST 7219 Humanities Data Analysis: Data analysis in the humanities presents challenges of scale, interpretation, and communication distinct from the social sciences or sciences. It also, some argue, opens up new opportunities for creative storytelling and narrativity. This seminar will explore the emerging practices of data analysis in the digital humanities from both a critical and a practical perspective. What light can algorithmic approaches shed on live questions in humanistic scholarship? What new forms of research are enabled by the use of data? What sort of data do practicing humanists want museums and libraries to make available? Our goal in this class will be to explore the new emerging forms of data analysis taking place in humanities scholarship, both in terms of applying algorithms and in terms of better investigating the presuppositions and biases of the digital object. We’ll aim to come out much more sophisticated in the use of computational techniques and much more informed about how others might use them.
HIST 7239 Digital Space and Place: What is the “spatial turn” and how does it intersect with the digital humanities? This course offers an introduction to major theories of space and place and how they are being applied through technologies such as Geographic Information Systems (GIS), data visualizations, and 3D modeling. This is a hands-on course in which students will develop digital skillsets, including creating online maps and visualizations, analyzing spatial datasets, and designing virtual exhibits – all within a humanities framework of spatial theory. Classes will consist of a combination of discussion, practicums, walking tours, and field trips. Students will emerge from the course with spatial literacy: the ability to critically read, analyze, and interpret a wide variety of spaces and places (neighborhoods, landscapes, museums) along with ways of representing these geographies in maps, narratives, video games, and other media.
HIST 7370 Texts, Maps, and Networks: Readings and Methods for Digital History: Introduces the methods and practice of history in a digital age. Offers students an opportunity to see the wide variety of work being done computationally by historians and other humanists today and to obtain the background to be creative producers of new work and critical consumers of existing projects. The rise of computing technology and the Internet has the potential to reshape all parts of historical practice, from curation to research to dissemination. Examines the historian’s craft in three primary domains: the creation of digital sources, the algorithmic transformations that computers can enact on cultural materials like texts, and the new ecologies of publishing and scholarly communication made possible by new media.
HIST 7250 Topics in Public History: This course surveys contemporary approaches to digital public history, centering ideas of publics and their impact on subject matter, methodologies, design choices, ideas of audience, and forms of engagement and collaboration. While we will consider projects and approaches explicitly situated as forms of “public history” and supported by academic institutions, students will also review innovative digital work happening across a wide range of nonacademic contexts: museums, public libraries, community archives, public art, podcasts, journalism, activism, among others. We will examine the ways that these initiatives are staffed, managed, and valued (identifying best as well as not-the-best practices). As part of the course’s investment in the pedagogical value that comes from collaborative work on active and public-facing digital projects, we will also work with the COVID-19 Archive on its approaches to documenting and curating the impact of the global pandemic on the Greater Boston area. Students will also have the opportunity to develop and refine their own prototypes and approaches to digital public history on topics that resonate with them.
INSH 5301 Introduction to Computational Statistics: Introduces the fundamental techniques of quantitative data analysis, ranging from foundational skills—such as data description and visualization, probability, and statistics—to the workhorse of data analysis and regression, to more advanced topics—such as machine learning and networks. Emphasizes real-world data and applications using the R statistical computing language. Analyzing and understanding complex data has become an essential component of numerous fields: business and economics, health and medicine, marketing, public policy, computer science, engineering, and many more. Offers students an opportunity to finish the course ready to apply a wide variety of analytic methods to data problems, present their results to nonexperts, and progress to more advanced course work delving into the many topics introduced here.
INSH 5302 Information Design and Visual Analytics: Introduces the systematic use of visualization techniques for supporting the discovery of new information as well as the effective presentation of known facts. Based on principles from art, graphic design, perceptual psychology, and rhetoric, offers students an opportunity to learn how to successfully choose appropriate visual languages for representing various kinds of data to support insights relevant to the user’s goals. Covers visual data mining techniques and algorithms for supporting the knowledge-discovery process; principles of visual perception and color theory for revealing patterns in data, semiotics, and the epistemology of visual representation; narrative strategies for communicating and presenting information and evidence; and the critical evaluation and critique of data visualizations. Requires proficiency in R.
INSH 6406 Analyzing Complex Digitized Data: Introduces cutting-edge ways of structuring and analyzing complex data or digitized text-as-data using the open-source programming language Python. Scholars across multiple disciplines are finding themselves face-to-face with massive amounts of digitized data. In the humanities and social sciences, these data are often in the form of unstructured text and un- or under-structured data. Encourages students to think about novel ways they can apply these techniques to their own data and research questions and to apply the methods in their own research, whether it be in academia or in industry.
INSH 7910 NULab Project Seminar: Offers students an opportunity to learn and use digital humanities methods with others in groups and across disciplines in the collaborative space of the NULab seminar. May be repeated up to three times.
JRNL 6340 Fundamentals of Digital Storytelling: Offers students an opportunity to learn the fundamentals of digital journalism and to place those skills within the context of a changing media environment. Studies multimedia tools within an intellectual framework—i.e., offers students an opportunity to learn hands-on skills and also to study best practices and theory. May include guest speakers and a consideration of the future of news. Requires students to produce a final project that consists of storytelling across a range of digital platforms.
JRNL 6341 Telling Your Story with Data: Explores select topics in data journalism and supports data-driven storytelling projects of various kinds. Offers students an opportunity to learn how to navigate the often-competing demands of rigorous analysis and accessible narrative and storytelling. Course units are designed to foster moderate technical learning of applications and software, incorporate theories from relevant fields in data visualization and data science, and emphasize storytelling for broad public audiences.
JRNL 6355 Seminar in Investigative Reporting: Introduces students to the world of investigative reporting as it is practiced at major metropolitan newspapers. Asks students to work as members of investigative reporting teams and introduces them to advanced reporting techniques and standards in the classroom. Provides an opportunity to learn how ideas for investigative reporting projects are developed; how to identify and interpret public records and online databases; and how to do interviews and write investigative stories. Working in small teams, the students are given an opportunity to develop and write investigative stories for publication.
POLS 7334 Social Networks: Offers an overview of the literature on social networks, with literature from political science, sociology, economics, and physics. Analyzes the underlying topology of networks and how we visualize and analyze network data. Key topics include small-world literature and the spread of information and disease. Students who do not meet course prerequisites may seek permission of instructor.
PPUA 5263 Geographic Information Systems for Urban and Regional Policy: Studies basic skills in spatial analytic methods. Introduces students to some of the urban social scientific and policy questions that have been answered with these methods. Covers introductory concepts and tools in geographic information systems (GIS). Offers students an opportunity to obtain the skills to develop and write an original policy-oriented spatial research project with an urban social science focus.
PPUA 5301 Introduction to Computational Statistics: Introduces the fundamental techniques of quantitative data analysis, ranging from foundational skills—such as data description and visualization, probability, and statistics—to the workhorse of data analysis and regression, to more advanced topics—such as machine learning and networks. Emphasizes real-world data and applications using the R statistical computing language. Analyzing and understanding complex data has become an essential component of numerous fields: business and economics, health and medicine, marketing, public policy, computer science, engineering, and many more. Offers students an opportunity to finish the course ready to apply a wide variety of analytic methods to data problems, present their results to nonexperts, and progress to more advanced course work delving into the many topics introduced here.
Type of Program
Links and Resources