The NULab faculty teach a wide variety of courses in the domain of digital humanities and computational social sciences, including introductory and advanced courses on quantitative methods, mapping, book and media history, network science, literary analysis, and information design.

ARTG 5100 Information Design Studio 1
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.

ARTG 5120 Information Design Research Methods
Examines qualitative and quantitative research methods pertinent to information communication systems. Through discussion and writing activities, offers students an opportunity to investigate varied inquiry toward the development of researchable questions, argument formation, and assessment methodologies.

ARTG 6100 Information Design Studio 2 (4SH)
Instructor: Dietmar Offenhuber and external guests
The studio class focuses on interactive and time-based techniques for information visualization across different contexts and scales. Using the visual computing language Processing, students will conceptualize and develop interactive visualizations and information displays, ranging from the personal to the urban scale.

BUSN 6320 Business Analytics Fundamentals (1SH)
Instructor: Christoph Riedl
Introduces the key concepts of data science and data analytics as applied to solving data-centered business problems. Emphasizes principles and methods covering the process from envisioning the problem; applying data science techniques; deploying results; and improving financial performance, strategic management, and operational efficiency. Includes an introduction to data-analytic thinking, application of data science solutions to business problems, and some fundamental data science tools for data analysis. Prereq. Business students only. 

CS 6120 Natural Language Processing (4SH)
Instructor: David Smith
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. Prereq. Restricted to students in the College of Computer and Information Science. 

CS 6200 Information Retrieval (4SH)
Instructor: David Smith
Provides an introduction to information retrieval systems and different approaches to information retrieval. Topics covered include evaluation of information retrieval systems; retrieval, language, and indexing models; file organization; compression; relevance feedback; clustering; distributed retrieval and metasearch; probabilistic approaches to information retrieval; Web retrieval; filtering, collaborative filtering, and recommendation systems; cross-language IR; multimedia IR; and machine learning for information retrieval. Prereq. Restricted to students in the College of Computer and Information Science.

DSSH 6302 – 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 in order to support insights relevant to the user’s goals. Topics include 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, finally, the critical evaluation and critique of data visualizations.

ENGL 1450 Technology, Literature, and New Media 
Instructor: Ryan Cordell
Introduces the historical interplay among technology, new media, and literature. Examines how new innovations change the way readers engage with texts, how society wrestles with the implications of those changes, and how writers produce new kinds of literature in response. Highlights these historical moments of change in an effort to help students better engage contemporary technological and literary upheaval. Studies technologically engaged literary works from a variety of genres, including fiction, poetry, film, and video games. Offers students an opportunity to explore new technologies that change how scholars research literature; to develop critical skills for conducting effective online research; and to develop skills for analyzing and interpreting texts in a range of media. 

ENGL 2150 Literature and Digital Diversity
Instructors: Elizabeth Dillon and Sarah Connell
This seminar will explore the use of digital tools for analyzing, preserving, and transforming literature and literary culture. Whose work is preserved and whose work isn’t? Whose stories are told and whose are not? Do digital tools enable us to bring more diversity to the literary past and present? We will read key texts from Shakespeare (The Tempest) to Shelley (Frankenstein) and learn how these texts have been transformed into digital forms. And we will try our own hands at these digital transformations as well. We will also use digital methods to analyze these texts and their contexts. Together we will consider how digital tools enable us to reconsider issues of gendered authorship, racial representation, and the links between archives and authority in the past and today.

ENGL 3340 Technologies of Text
Instructor: Ryan Cordell
When you hear the word “technology,” you may think of your computer or smart phone. You probably don’t think of the alphabet, the book, or the printing press: but each of these was a technological innovation that changed dramatically how we communicate and perhaps even how we think. Literature has always developed in tandem—and often in direct response to—the development of new media technologies—e.g. moveabe type, the steam press, the telegraph, radio, film, television, the internet. Our primary objective in this course will be to develop ideas about the ways that such innovations shape our understanding of texts (both classic and contemporary) and the human beings that write, read, and interpret them. We will compare our historical moment with previous periods of textual and technological upheaval. Many debates that seem unique to the twenty-first century—over privacy, intellectual property, information overload, and textual authority—are but new iterations of familiar battles in the histories of technology, new media, and literature. Through the semester we will get hands-on experience with textual technologies new and old through labs in paper making, letterpress printing, data analysis, and 3D printing. The class will also include field trips to museums, libraries, and archives in the Boston area.

ENGL 7370 Topics in Digital Humanities: The Shape of Data in the Humanities
Instructor: Julia Flanders
Data underlies every digital resource, and the shaping of that data determines how those resources work. Some data is carefully designed and shaped for specific goals; some data has almost no shaping at all. The modeling systems we use to give shape to our data are the power tools of the digital humanities, and the modeling choices we make — or others make for us — have a profound effect on the kinds of research we can do. What kinds of modeling are appropriate for different kinds of research materials, and how are these modeling systems developed? This course will give us a very close look at these questions, with particular focus on two widely used tools: the Omeka publishing platform and the TEI Guidelines. Students will engage deeply with the different ways these and other tools shape our research materials. Through the course assignments students will design and implement the components of a digital project, including a prototype Omeka site, sample encoded files, a schema, and a project proposal. Students will come away with a strong grounding in concepts of data modeling for the digital humanities, with broad applicability to historical and literary studies. No prior familiarity with XML or digital humanities is required or assumed.

HIST 7219: Humanities Data Analysis
Instructor: Ryan Cordell
Humanities scholars in the 21st century grapple with ever larger and more diverse kinds of evidence created through the massive digitization of historical, literary, and cultural heritage materials. The creation, representation, analysis, and visualization of humanistic data constitute engagingly thorny challenges which this seminar will explore from both practical and critical perspectives. Our most ambitious goal in this class will be to explore the new emerging forms of data analysis taking place in humanities scholarship, both by learning to apply algorithms and learning to better investigate the presuppositions and biases of digital objects. We will aim to emerge more sophisticated in our use of computational techniques and much more informed about how other scholars use them. Students will develop final projects using a data set pertinent to their own research or areas of interest.

HIST 7370 – Texts, Maps, and Networks: Readings and Methods for Digital History
Instructor: Benjamin Schmidt
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.

INSH 7910 NULab Project Seminar
Instructors: Julia Flanders and Elizabeth Maddock Dillon
This one-credit practicum course will explore the broad domains of digital humanities and computational social science, with special attention to the distinctive methods, research questions, tools, and assumptions at work. Specific research areas might include text mining, visualization, text encoding, network science, mapping and GIS, and any other areas of shared interest. Participants will be encouraged to draw on work they are doing for other classes or research projects. No prior technical experience or familiarity with the field is required, but participants should be prepared to identify an area of research interest that is connected in some way with the general domain of digital humanities, computational social science, and related fields.

INSH 1500 Digital Methods for Social Sciences and Humanities
Instructor: Laura Nelson
Introduces programming skills and computational methods through application to topics in the social sciences and humanities. Methods include computational text analysis, network analysis, mapping software and analysis, computational approaches to data, big data, and/or social simulation. Offers students an opportunity to develop an understanding of the use and significance of computational tools for social sciences and humanities. No previous programming experience required.

INSH 2102 Bostonography: The City through Data, Texts, Maps, and Networks
Instructors: Benjamin Schmidt, Nicholas Beauchamp, Dan O’Brien, Ryan Cordell
Uses Boston as a case study for integrating computational methods with the social sciences and humanities to provide new insights into major cultural, historical, and societal questions as they relate to and extend beyond the city of Boston. Through lectures, discussions, and labs, the course examines a variety of data sets that measure geographic, historical, literary, political, civic, and institutional landscapes. Offers students an opportunity to combine analytical tools, such as geospatial mapping, data visualization, and network science, with readings, hands-on class activities, and museum or site visits, enabling a comprehensive view of complex cultural and social phenomena.

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.

Texts, Maps, Networks: Digital Literary Analysis
Instructor: Ryan Cordell
Literary scholars have long studied the linguistic, bibliographic, and social codes of texts, but in recent years new technologies have greatly expanded how those texts can be explored, both in research and the classroom. One can discuss a book, a chapter, a line, or a single word: or one can model patterns across entire corpora. In “Texts, Maps, Networks,” we will investigate both the affordances and potential pitfalls of digital research methods and pedagogy, from encoding and text mining to mapping and network analysis. Our class sessions will balance theory and praxis, moving between discussion of readings and humanities labs.

IS 4700/CS 5750 – Social Computing
Instructor: Alan Mislove and Christo Wilson
The course focuses on investigating the city and its spatial, social and economic dynamics through the lens of data and visual analytics. Students will develop visualization projects using large public datasets and develop knowledge about visual methods for analyzing data and communicating results.

Recently, online social networking sites have exploded in popularity. Numerous sites are dedicated to finding and maintaining contacts and to locating and sharing different types of content. Online social networks represent a new kind of information network that differs significantly from existing networks like the Web. For example, in the Web, hyperlinks between content form a graph that is used to organize, navigate, and rank information. The properties of the Web graph have been studied extensively, and have lead to useful algorithms such as PageRank. In contrast, few links exist between content in online social networks and instead, the links exist between content and users, and between users themselves.

The resulting graph is used to connect and to communicate. Unlike previous networks, graphs in online social networks intermingle people and content, allow systems designers to relate the reputation of content to the reputation of users, and vice versa. It opens the door for new types of systems, new ways of solving longstanding problems, and new security attacks and vulnerabilities.

This course provides a detailed look at popular social information systems, including from online social networks (Facebook, MySpace, Orkut), blogging and microblogging platforms (LiveJournal, Blogger, Twitter), social recommendation engines (Digg, Reddit,, collaborative organization (Wikipedia), and content sharing sites (Flickr, YouTube). Coursework includes studying models (both formal and sociological) of social information systems, and the application of them both in theory and by analyzing real data from social network interactions.

The graduate version of this courses places greater emphasis on the computing infrastructure that underlies the emerging systems. Focuses on building scalable systems for managing and manipulating large amounts of data, on ensuring privacy for the users, on designing and using interfaces for third-party applications, and on leveraging the mobile nature of the access mechanisms that many users use. A course project of the students choosing will be expected.

JRNL 6341 Telling Your Story With Data
Instructor: John Wihbey
Explores select topics in data journalism and support 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 6340 – Fundamentals of Digital Journalism
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.

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.

PPUA 5262 – Big Data for Cities – Visual Data Mining Strategies (3 or 4SH)
Instructor: Dietmar Offenhuber
The course focuses on investigating the city and its spatial, social and economic dynamics through the lens of data and visual analytics. Students will develop visualization projects using large public datasets and develop knowledge about visual methods for analyzing data and communicating results.

PPUA 6301 – Introduction to Computational Statistics
Instructor: Nick Beauchamp
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.