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Courses

Fall 2021

 
  Monday Tuesday Wednesday Thursday
4:15 - 6:15 PM

 

DATA 74000 - Data, Culture and Society (Online), Prof. Behar, #56336   DATA 71000 - Data Analysis Methods (Online), Prof. Everson, #56337
6:30 - 8:30 PM DATA 73000 - Visualization and Design (Hybrid), Prof. McSweeney, #56272 DATA 70500 - Working with Data: Fundamentals (Online), Prof. Shortell, #56538 DATA 73200 - Interactive Data Visualization (Hybrid), Prof. Frymire, #56335 DATA 70600 - Special Topics in Computational Fundamentals: JavaScript (Online), Prof. Zweibel, #64495, (6:30-7:30 P.M.)

 

Schedules by Semester

Fall 2021

DATA 70500 - Working with Data: Fundamentals (Online) #56538

Tuesday, 6:30 - 8:30 PM, 3 Credits. Prof. Tim Shortell (Shortell@brooklyn.cuny.edu)
DATA 73500 satisfies as a Data Analysis distribution core course

Note: This course will be online with synchronous class sessions.

This course covers the fundamentals of working with data. Students will be introduced to key disciplines that provide techniques used for working with small, medium and big data today—classical statistics, contemporary data science, machine learning, and data visualization. They will learn about different data types; what constitutes a valid dataset that can be analyzed quantitatively; how data should be formatted to create a valid dataset. The course will also explore fundamental theoretical questions that arise when we attempt to represent social or cultural phenomena as data. Particular attention will be focused on working with social network services data, user generated content, and other types of data about societies and individuals that have emerged recently (such as sensor data) and massive media datasets (images, video, text, sound, code, etc.). The course will explore fundamental database technologies and more recent techniques for working with real-time data flows.

The “data revolution” has transformed the way we understand and interact with the world around us. The availability of large datasets, progress in computer hardware and software, and use of the web to share data and acquire it from numerous sources (including social network services, libraries, museums, city governments, non-profits, etc.) has created many new possibilities in many fields including computer science, social science, humanities, business, economics, and medicine. These developments have also led to the emergence of a number of new research fields in the end of 2000s: social computing, computational social science, digital humanities, cultural analytics, and culturomics. This course introduces students to fundamental concepts and practical techniques and skills needed to work with data. We'll combine a critical view of data with examples that illustrate the logic of analysis.

We’ll begin with a broader examination of data and society. How do data practices in contemporary institutions shape communities and society? Then we’ll take a look at some of the tools used by data analysts and data scientists to produce knowledge in various settings, including survey data, demographic data and other forms of open data relevant to public policy, and social media.
 

DATA 70600 - Special Topics in Computational Fundamentals: JavaScript (Online) #64495

Thursday, 6:30 - 7:30 PM, 1 Credit. Prof. Stephen Zweibel (Szweibel@gc.cuny.edu)

Note: This is a 1-credit, 1-hour lab course, with synchronous online class sessions. 

This is a basic introduction to JavaScript, which is the programming language of the web. The class is designed for anyone interested in developing a website, or creating an interactive data visualization. By the end of this course, you will be able to read JavaScript you find online, and adapt it to your needs. You will have an opportunity to work with common JavaScript libraries/tools.

 

DATA 71000 - Data Analysis Methods (Online) #56337

Thursday, 4:15 - 6:15 PM, 3 Credits. Prof. Howard Everson (HEverson@gc.cuny.edu)
Website
DATA 71000 satisfies as a Data Analysis distribution core course

Note: This course will be online with synchronous class sessions. 

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 73000 - Visualization and Design (Hybrid) #56272

Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Michelle McSweeney 
(michelleamcsweeney@gmail.com)  
Cross-listed with DHUM 73000
DATA 73000 satisfies as a Data Visualization distribution core course

Note: Hybrid, with option to take purely online. In-person class dates are 8/30, 9/13, 9/20, 10/11, 10/18, 11/8, 11/15, 11/22, and 12/13. Online synchronous class dates are 10/14, 11/1, and 12/6. 

Data is everywhere and the ability to manipulate, visualize, and communicate with data effectively is an essential skill for nearly every sector—public, private, academic, and beyond. Grounded in both theory and practice, this course will empower students to visualize data through hands-on experience with industry-standard tools and techniques and equip students with the knowledge to justify data analysis strategies and design decisions.

Using Tableau Software, students will build a series of interactive visualizations that combine data and logic with storytelling and design. We will dive into cleaning and structuring unruly data sets, identify which chart types work best for different types of data, and unpack the tactics behind effective visual communication. With an eye towards critical evaluation of both data and method, projects and discussions will be geared towards humanities and social science research. Regardless of academic concentration, students develop a portfolio of interactive and dynamic data visualization dashboards and an interdisciplinary skill set ready to leverage in academic and professional work.

By the end of this class, students will be able to: 
  • Build interactive data visualization dashboards that answer a clear and purposeful research question;
  • Choose which chart type works best for different types of data; 
  • Iterate with fluidity in Tableau Software leveraging visualization, aesthetic, and user interface best practices; 
  • Structure thoughtful critiques and communicate technical questions and solutions; Leverage collaborative tools, including Tableau Public, Wordpress, and repositories of public data sets;
  • Contribute to the broader conversation about digital practices in academic research;
  • Critically read a wide range of chart types with an eye for accuracy, audience, and effectiveness; 
  • Identify potential weaknesses in the collection methods and structure of underlying data sets Locate the original source of a visualization and its data.
 

DATA 73200 - Interactive Data Visualization (Hybrid) #56335

Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Ellie Frymire (ellie.frymire@gmail.com)
Website: Two-N
DATA 73200 satisfies as a Data Visualization distribution core course

Note: In-peron class dates are 8/25, 10/27, 11/3, 11/10, 11/17, 11/24, 12/1, and 12/8. Online sessions will be synchronous.

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. 
As this course focuses heavily on learning how to make custom charts with D3.js, it assumes that students already have a working familiarity of HTML/CSS and basic JavaScript. Additionally, it is recommended that students feel comfortable working with git-based version control (Github, Gitlab. etc.) prior to starting this course.
 

DATA 74000 - Data, Culture and Society (Online) #56336

Tuesday, 4:15 - 6:15 PM, 3 Credits, Prof. Katherine Behar (katherine.behar@baruch.cuny.edu)
DATA 74000 satisfies as a Data Studies distribution core course

Note: This course will be online with synchronous class sessions.

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?
 

Summer 2021

Past Courses

Spring 2021

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)
Website

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 (aucher.serr@gmail.com)
Website: Two-N

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.

As this course focuses heavily on learning how to make custom charts with D3.js, it assumes that students already have a working familiarity of HTML/CSS and basic JavaScript. Additionally, it is recommended that students feel comfortable working with git-based version control (Github, Gitlab. etc.) prior to starting this course.

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 (kferguson@qc.cuny.edu)
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 (shipeng.sun@hunter.cuny.edu)
Cross-listed with DHUM 73700 #64164
Website

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.

Fall 2020
Spring 2020
Fall 2019
Summer 2019
Spring 2019
Fall 2018
Summer 2018