Courses

TEAM-TAUGHT SEMINARS

Developed by the Integrative PhD Fellowship Program:

2019-20

FALL 2019 COURSES

Visual, Spatial, and Material Politics

Taught by Victoria Hattam (NSSR  –  Politics) and Georgia Traganou (Parsons – Art and Design History and Theory); GPOL Politics 6218; PGHT 5658

Our central aim is to explore politics beyond the hegemony of the word, and in doing so, to introduce students to new methods and conceptions of evidence to use in their research. We will begin with space and visuality, but over the course of the semester, we will introduce questions of materiality, sound, and affect, aiming to explore the ways in which material and performative practices have the capacity to articulate political arguments. Throughout the class, we will consider whether embodied processes are different in nature from the cognitive processes of deliberation necessary for the articulation of verbal political messages. Do they, as Jane Bennett suggests initiate “moments of sensuous enchantment” that provide “the motivational energy needed to move selves from the endorsement of ethical principles to the actual practice of ethical behaviors”? To ground our explorations, the class will focus on specific political sites broadly conceived, from those of state authority and political dissent to sites of prefigurative politics, where we will look for the relations between political economies, material culture, art and artifacts. Research topics might derive from students’ own thesis projects, or from collective interests of students in the class, and they will be developearguments. Throughout the class, we will consider whether embodied processes are different in nature from the cognitive processes of deliberation necessary for the articulation of verbal political messages. Do they, as Jane Bennett suggests initiate “moments of sensuous enchantment” that provide “the motivational energy needed to move selves from the endorsement of ethical principles to the actual practice of ethical behaviors”? To ground our explorations, the class will focus on specific political sites broadly conceived, from those of state authority and political dissent to sites of prefigurative politics, where we will look for the relations between political economies, material culture, art and artifacts. Research topics might derive from students’ own thesis projects, or from collective interests of students in the class, and they will be developed

Design and Urban Ecologies Methods 3

Taught by: Eric Brelsford; PGUD Urban Design 5260

Urban Research Methods 3 introduces geospatial technologies as tools tailored to the analysis and support of participatory projects and community-based research for urban and regional spaces. The class explores open source desktop and web-based geospatial tools for research, design, and visualization of urban conditions, as well as strategies for intervention in urban communities. Students will gain valuable experience in harnessing open data and creating innovative mapping tools for the empowerment local urban communities.

Data Structures

Taught by: TBA; PGDV Data Visualization 5110

This course covers the fundamentals of the database, semi-structured data, and unstructured data. Students will gain familiarity with data visualization concepts, techniques, and tools, including acquisition, augmentation, and restructuring; data storage and aggregation; access to parallel and distributed computing; high-volume data, disparate sources, and performance; and streaming data and real time and dynamic queries. Open to: Masters degree in Data Visualization majors only; others by permission of Data Visualization program.

Big Data and the Media

Taught by: Robert Berkman; NMDM Media Management 5321

“Big Data” refers to the enormous amount of quantitative, statistical, personal and other unstructured data being generated by people, companies, processes, and even objects. Data analytics can crunch the data in novel ways to uncover patterns and offer fresh insights for making better business decisions. This has critical implications for the media industry. New types of audience data, including social, mobile, and real-time data, are being created, and that is altering the kinds of tools, software, and strategies needed for measuring and making sense of it all.

While Big Data can be a potential boon to researchers, there are cautions. First, how does one separate correlation from causation; pinpoint what truly matters; and take meaningful action? We may also face a temptation to rely on quantitative data and processes when it is not the most useful tool. There are also ethical dimensions in the use of socially generated big data, including scraping of online conversations and assumptions made about customers’ preferences and behaviors.

This course will explore the potential of data and analytics for media marketers and researchers, both through a popular cultural lens (for example, via Nate Silver’s popular book The Signal and the Noise), as well as by focusing on how new types of data streams are changing media market research, including new ethical concerns . We’ll also look at the ability to make predictions from the data (predictive analysis) and how that is already changing the industry. This section is open to Master of Science and graduate certificate Media Management students only. All other graduate students may request access three weeks before the start of the course by emailing mediastudiesadvising@newschool.edu

Media Research Methods

Taught by: Lauhona Ganguly; NMDS Media Studies 5010

This course focuses on developing specific research methods skills used in media analytics. Students acquire the necessary skills to develop samples for media research and to conduct original research by focusing on two research methods: interviewing and focus groups. Analytics is sometimes seen as purely quantitative, but interviewing and focus groups are invaluable qualitative methods for uncovering user’s media experiences. That knowledge can then help determine what data to analyze. Students will do original research by learning about sampling strategies, then applying those to either interviews or focus groups. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email mediastudiesadvising@newschool.edu for access.

Participatory Research and Creative Inquiry

Taught by: TBA; NMDS Media Studies 5288

This course explores participatory and qualitative approaches to designing and conducting social inquiry and behavioral research. The class focuses on applying such approaches to understanding the role of participatory media, digital narrative, and DIY cultures in social and community-based contexts, particularly in global settings. Students will examine case studies, theory and practice, as well as ethical considerations for conducting ethnographic fieldwork and qualitative research both online and in place-based communities. We will review approaches to designing qualitative studies, conducting participant observation, focus group sessions and semi-structured interviews, as well as handling informed consent, privacy, and confidentiality. The course also explores novel participatory modes of research leveraging digital media, narrative, mapping and creative expression in diverse socio-cultural settings. Finally, we examine methods for organization and analysis of qualitative data collected in the field to make sense of emerging research outcomes. Students will be expected to conduct brief exercises and devise suitable methods to propose a potential research study of interest. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email mediastudiesadvising@newschool.edu for access.

 

SPRING 2020 COURSES

Visualizing Uncertainty

Taught by Michael Schrober & Aaron Hill; GPSY 6422 Psychology

This seminar brings together data visualization and psychology graduate students to investigate new ways of representing and hypothesizing about data while rigorously questioning what conclusions can legitimately be drawn. How should we think about where the data came from and the methods by which they were generated? What sources of potential measurement error should psychologists and data scientists be concerned about? When can we trust that data collected from nonprobability samples generalize to a full population? When are patterns that emerge in exploratory data visualization trustworthy? How can skepticism and questions about data be communicated with the potential audiences for a visual representation of data? How can we better visualize measurement error and multivariate confidence intervals? Class sessions will combine discussion of academic articles with hands-on examination of existing data sets and practical examples. Psychology and data visualization students will be paired to carry out two hands-on projects during the semester, ideally using their own data from class or thesis projects (although having one’s own data is not required). From these projects, students will gain experience in communicating with collaborators with quite different backgrounds and expertise. Students are only expected to have background knowledge from their own discipline; data visualization students are not expected to have any psychology expertise, and psychology students are not expected to have any coding or design expertise. **Psychology students should already have taken a graduate-level Research Methods course, and Data Visualization students should already have proficiency in interaction design.** The course counts as an elective and satisfies the seminar requirement for the Psychology PhD programs; it counts toward any of the umbrella courses for Data Visualization students.

GIS for International Crises, Development and the Environment

Taught by Stephen Metts; NINT 5380 International Affairs
This course provides an introduction to desktop and web-based GIS software via real-world scenarios and research questions in humanitarian relief, international development, and environmental issues. In particular, students will learn to analyze, map, and publish spatial information using powerful GIS tools. Students will develop skills in web and paper-based cartography, collaborative online mapping, spatial data analysis, mobile phone data collection, and using and manipulating satellite and aerial imagery.

Transforming Data: Cultural Strategies in Datamining

Taught by Jonathan Thirkield; NMDS 5517 Media Studies
From data capture to data visualization, the process of data transformation is a search for meaning through dozens to hundreds to millions of units of information. This course is both a conceptual and practical introduction to contemporary datascape, big and small. The focus will be on the two essential sides of data transformation: making data readable for humans and for machines. Through presentations, readings, and discussions, students will examine a variety of approaches to data research from data structures, to mining and aggregation, to visualization. In pratical projects, students will work with publicly available data sets as well as build their own. Computational methods covered by this class will include APIs, text and image libraries, quantitative methods, maps and GeoCoding. Students will be introduced to programming languages for data management (MySQL and JavaScript), and for visualization (Processing/PSjs). Prior programming experience may be advantageous, but is not necessary. Pre-Requisite: Media Design (NMDS 5008) or instructor permission. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email mediastudiesadvising@newschool.edu for access.

Storytelling with Data

Taught by Maria Massei-Rosato; PSAM 5015 AMT
In the wake of data science and data visualization tools, great emphasis has been placed on the visual aspect of storytelling, however, little attention has been paid to the narrative. Drawn on the work of Aristotle, Ernest Hemingway, Edward Tufte, Robert McKee, and Cole Nussbaumer Knaflic, this class will examine the narrative structure and how it can be applied to create compelling stories through a combination of data, visuals, and narrative. Emphasis will be placed on analyzing stories to draw out concepts and applying these concepts to hands-on assignments to understand what works and what doesn’t. The final project will culminate in application of these concepts to the explanatory process of storytelling with data. Open to all University Graduate degree students. Some seats have been reserved for MS Data Visualization students.

Machine Learning

Taught by Aaron Hill; PSAM 5020 AMT
Machine learning is the systematic study of algorithms and systems that improve their knowledge and performance with experience. Collecting and analyzing data through machine learning algorithms and models can uncover complex patterns in massive amounts to data to make more accurate predictions and to reveal coherent dimensions. In this course students will consider classification, regression, clustering, subgroup, and association models to predict and describe, using supervised and unsupervised learning methods. Special attention will be given to models and techniques that aid and support the visualization of complex data. Open to: All University Graduate students. Some seats have been reserved for MS Data Visualization majors.