|4:15 - 6:15 PM
DATA 78000 
Alternative Data Cultures
|DATA 74000 
Data, Culture and Society
||DATA 71000 
Data Analysis Methods
|6:30 - 8:30 PM
||DATA 73200 
Interactive Data Visualization
||DATA 78000 
Introduction to GIS: Methods and Applications
Note: All Spring 2021 courses will be online.
DATA 71000 - Data Analysis Methods #64005
Thursday, 4:15 - 6:15 PM, 3 Credits, Prof. Howard Everson (HEverson@gc.cuny.edu)
This course is intended for students enrolled in the MS Program in Data Analysis & Visualization. The goal of the course is to provide students with an introduction to basic statistical techniques for analyzing numerical or quantitative data. The emphasis throughout will be on the development of statistical reasoning, i.e., thinking like a data scientist. The course will develop students’ understanding of the fundamental concepts underlying modern statistics thereby allowing for the analysis of a variety of data types and data sources, as well as gaining insights through the visualization of trends and patterns in data. To achieve these goals students will be introduced to the principles of probabilistic reasoning, sampling, experimental design, descriptive statistics and statistical inference. Students will explore various statistical methods and techniques for analyzing data and practice applying these methods to real-world data-driven problems. Practical topics will include: descriptive and inferential statistical methods, sampling and data collection, and an array of statistical modeling techniques such as correlational analysis, multivariate regression, logistic regression, and exploratory data analysis. Students will become familiar with a variety of statistical software packages including, Excel, SPSS, Stata and R.
DATA 73200 - Interactive Data Visualization #64006
Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Aucher Serr (email@example.com)
Interactive Data Visualization is one of the most important forms of communication today — allowing users to better engage with data, detect patterns, and quickly gain insight into complicated topics. This course will introduce students to the tools, skills, and concepts necessary for making state-of-the-art interactive data visualizations. Using web-based technologies including HTML, CSS, and D3.js, students will learn to create engaging and effective information displays, grounded in the science of visual perception and best practices in visual mapping and accessibility. Throughout the semester, students will work towards creating a portfolio of beautiful and analytically sound data visualizations, while also developing their own iterative design process.
DATA 74000 - Data, Culture and Society #64007
Tuesday, 4:15 - 6:15 PM, 3 Credits, Prof. Katherine Behar (Katherine.Behar@baruch.cuny.edu)
Big data and computational methods for its analysis are changing scientific and humanities research, financial markets, political campaigning, higher education, and countless other areas, and also affect our everyday lives. Our daily existence is increasingly structured by software systems that process massive amounts of data and generate results such as music and book recommendations, search engine inputs, car routes, airline prices, etc.
in this course, we explore the social, political, and cultural impact of reliance of our society on massive (and often real-time) data analysis. We will discuss the concepts behind data collection, organization, analysis, and publication. We will also discuss possibilities, limitations, and implications of using big data-centric methods in social science and humanities research, and the already developed work in computational social science, digital humanities and cultural analytics fields. Students will become familiar with the history and basic concepts of the fundamental paradigms developed by modern societies to analyze patterns in data--statistics, visualization, data mining, and machine learning.
Finally, we also want to ask general questions about society and culture in a data-centric society. The arrival of social media and the gradual move of knowledge and media distribution and cultural communication to digital networks in the early 21st century has created a new digital landscape which challenges our existing methods for the study of and assumptions about culture. What new theoretical concepts do we need to deal with the new scale of born-digital culture? What data analysis and visualization techniques developed by industry and sciences are most useful for cultural analysis? How can we use big cultural data to question what we know about culture and generate new questions?
DATA 78000 - Special Topics: "Alternative Data Cultures" #64008
Monday, 4:15 - 6:15 PM, 3 Credits, Prof. Kevin Ferguson (firstname.lastname@example.org)
Cross-listed with DHUM 78000 #64163
This course will examine alternative trajectories of data visualization that lie outside of the traditional approaches that aim to represent data as neutrally and naturally as possible. Beginning with Lisa Samuels and Jerome McGann's concept of “deformance”—a new scholarly performance of a text that eschews solely searching for a hidden interpretation—we will survey a variety of ways that data visualization centered on humanistic inquiry can be recontextualized, remixed, and otherwise bent, broken, and glitched in order to generate new knowledge. By considering how data visualization might fruitfully embrace subjective perspectives in order to create meaning, this course will ask students to more deeply consider how and why we visualize complex data sets, including sets of objects such as literary corpora, photographs, motion pictures, and music.
Throughout the course we will explore the intersection of aesthetics, art, and alternative ways of “performing” data to reveal new insights, drawing on surrealist and other avant-garde traditions that begin with defamiliarization as a critical practice. In addition to readings and models of new perspectives on data visualization, students will complete experimental projects visualizing a variety of texts, which may include condensing feature films to single images, comparative movie “barcodes,” glitching historical images, and other experimental exploratory data visualization. Students may complete exploratory projects in ImageJ (Java), Python, and/or R, although no prior expertise is required of students.
Readings may include: Johanna Drucker, Mark Sample, Zach Whalen, Jason Mittell, Deb Verhoeven, Michael J. Kramer, Stephen Ramsay, Lev Manovich, Julia Flanders, Eric Hoyt, Shane Denson, Giorgia Lupi and Stefanie Posavec, Virginia Kuhn, and Bethany Nowviskie.
DATA 78000 - Special Topics: "Introduction to GIS: Methods and Applications" #64009
Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Shipeng Sun (email@example.com)
Cross-listed with DHUM 73700 #64164
This course combines an introduction to basic cartographic theory and techniques in humanities contexts with practical experience in the analysis, manipulation, and the graphical representation of spatial information. The course examines the storage, processing, compilation, and symbolization of spatial data; basic spatial analysis; and visual design principles involved in conveying spatial information. Emphasis is placed on digital mapping technologies, including online and offline computer based geographic information science tools.