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 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.

ARTG 6110 Information Design Theory and Critical Thinking (4SH)
Instructor: Hugh Dubberly
This seminar class examines theoretical foundations and models of information visualization and delivery systems. Structured in three individual, two-day workshops, students will evaluate the concepts and models through diagrammatic exercises, discussions and writing activities.

ARTG 6310 Design for Behavior and Experience (4SH)
Instructor: Isabel Meirelles
This studio class provides an overview of behavior and experience design in the context of visualization of information. The course will be delivered as studio projects, individual and class critiques, lectures, discussions, and readings. For the main project, students will work in small teams of two or by themselves to define, analyze and design visualization experiences with the purpose to enrich one’s interaction with an existent social media application.

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.

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 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.

INSH 7910 NULab Workshop
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.

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.

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.