The NULab faculty teach a wide range of digital humanities and computational social sciences courses. These courses cover topics including digital humanities theory and practice, digital literacy, text analysis, mapping, data visualizations, and text encoding. Here are some of the courses being offered at Northeastern:
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 permission of instructor.
ARTG 6100 Information Design Studio 2: Dynamic Mapping and Models
Continues the exploration of data representations in a variety of media. Focuses on interactive and time-based techniques. Emphasizes computational methods of data collection, manipulation, and encoding. Requires graduate standing or permission of program coordinator or instructor. May be repeated once.
BUSN 6320 Business Analytics Fundamentals
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.
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 6200 Information Retrieval
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.
ENGL 1450 Reading and Writing in the Digital Age
Grapples with the long and sometimes tumultuous relationship between literature—including fiction, poetry, film, and video games—and new media technologies. Offers students opportunities to historicize and engage the social and literary upheavals of our own technological moment through reading, discussion, writing projects, and practicums that seek to develop skills for analyzing the data and metadata of texts through both qualitative and quantitative methods.
ENGL 2150 Literature and Digital Diversity
Focuses on the use of digital methods to analyze and archive literary texts, emphasizing issues of diversity and inclusion. Covers three main areas: text encoding, textual analysis, and archive construction. Considers literary texts and corpora, including works by well-known authors such as Shakespeare, together with collections by marginalized writers, including slave narratives and writings by early modern women. Offers students an opportunity to explore what counts as literature and how computers, databases, and analytical tools give substance to concepts of aesthetic, cultural, and intellectual value as inflected by race and gender.
ENGL 3340 Technologies of Text
Examines innovations that have reshaped how humans share information, e.g., the alphabet, the book, the printing press, the postal system, the computer. Focuses on debates over privacy, memory, intellectual property, and textual authority that have historically accompanied the rise of new media forms and genres. Offers students an opportunity to gain skills for working with texts using the rapidly changing tools of the present, e.g., geographic information systems, data mining, textual analysis.
ENGL 7370 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.
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.
INSH 1500 Digital Methods for Social Sciences and Humanities
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
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 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 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.
IS 4700 Social Information Systems
Analyzes popular social information systems, including online social networks, blogging platforms, recommendation engines, and content sharing sites. Studies the objectives, user interaction modes, policies, and design issues for social information systems. Introduces relevant theories, both computational and sociological, that model the behavior of social networks and their users. Offers students an opportunity to learn to apply such models, both theoretically and by analyzing real-world interaction data from social information systems, to answer questions such as: What causes users to form links? What mechanisms work best for encouraging collaboration? How does information spread through cyberspace? How can security and privacy goals be achieved?
JRNL 6341 Telling Your Story With Data
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. Students who do not meet course prerequisites may seek permission of instructor.
PPUA 5262 Big Data for Cities
Investigates the city and its spatial, social, and economic dynamics through the lens of data and visual analytics. Utilizes large public datasets to develop knowledge about visual methods for analyzing data and communicating results. Offers students an opportunity to develop a critical understanding of data structures, collection methodologies, and their inherent biases.