Courses

DATA 73000 - Visualization and Design (#55619)

In Person, Room 5383
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 core course.

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.

Objectives: 

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 70600 - Special Topics in Computational Fundamentals: Python (#55620)

Hybrid, Room 3207
Monday, 5:15 - 6:15 PM, 1 credit, Prof. Filipa Calado (fcalado@gradcenter.cuny.edu)
In-person dates: 1/30, 2/6, 2/21, 2/27, 4/24, 5/1, and 5/8. All other classes online.
Cross-listed with DHUM 70600

Note: This is a 1-credit, 1-hour lab course. Students may take a maximum of three 1-credit courses (a total of 3 credits) for elective credits. 

This course is an introduction to Python, a general-purpose programming language with increasing popularity among academics and in the industry. In the course, we will cover installations, different coding tools, working with text editors, the basics of the command line and how to run scripts on it. We will learn the fundamentals of programming languages, such as variables, functions, types, conditionals, and loops. After that, we will introduce Python for Text Analysis with the NLTK library and for Data Analysis with the Pandas library. This course has a very hands-on approach, and students are expected to engage with exploratory analysis both in the class and out of the class. No previous programming knowledge is required.

DATA 78000 - Special Topics: “Methods of Text Analysis” (#55618)

Hybrid, Room 5383
Monday, 4:15 - 6:15 PM, 3 credits, Prof. Lisa Rhody (lrhody@gc.cuny.edu)
In-person dates: 1/30, 2/6, 2/21, 2/27, 3/6, 5/15. All other classes online.
Cross-listed with DHUM 72500 

This course takes as its guiding questions: "Can there be such a thing as a feminist text analysis?" and "What does it mean to do computational text analysis in a humanities context?" Through reading and practice we will examine the degree to which problematic racist, sexist, colonialist, corporate, and gender-normative assumptions that activate algorithmic methods impact humanistic inquiry through text analysis, and how the humanist can formulate effective research questions to explore through methods of text analysis.

Taking a completely different approach to the topic "methods of text analysis," this course will consider what it means to "analyze" a "text" with computers within a humanistic context, with an emphasis on shaping effective research questions over programming mastery. How does the language of analysis draw on Western traditions of empiricism in which "the text" occupies a position of authority over other forms of representation? What is the difference between "text analysis" and "philology"? What is being "analyzed" when we count, tokenize, measure, and classify texts with computers? And, importantly, how do the questions we are asking align with the methods we are using?

The course will be organized according to the stages of the research process as articulated in our fist week reading, to be completed in advance of our first meeting: "How we do things with words: Analyzing text as social and cultural data," which can be downloaded here. While students will receive materials to help them learn Python and to develop their own text analysis projects, this will not be the objective of the course or the source of evaluation. However, students will be required to develop a literacy in Python and packages frequently used to perform text analysis. Students will be required to complete weekly Jupyter notebook assignments that have significant portions of text analysis activities already completed. Supplementary information about programming and text analysis will be provided to complete in a self directed way using a free DataCamp account. Final projects will include a portfolio of 14 completed Jupyter notebook assignments, an in-class debate, and a five to eight page position paper.

Exploring terms such as "non-consumptive" and "black box algorithms," this course takes up the affordances and costs of computationally enabled modeling, representation, querying, and interpretation of texts. We will ask questions such as, "Can you 'lead a feminist life' (Ahmed) that is heavily mediated by methods of text analysis?" Readings will include articles by Sarah Ahmed, Mary Beard, Meredith Broussard, Lauren Klein, Wendy Chun, Tanya Clement, Miriam Posner, Liz Losh, Tara MacPherson, Johanna Drucker, Andrew Goldstone, Safiya Noble, Bethany Nowviskie, Andrew Piper, Steve Ramsay, Laura Mandell, Susan Brown, Richard Jean So, and Ted Underwood.
 

DATA 74000 - Data, Culture and Society (#55614)

In Person, Room 5382
Wednesday, 6:30 - 8:30 PM, 3 credits, Prof. Kevin Ferguson (kferguson@qc.cuny.edu)

DATA 74000 satisfies as a Data Studies core course.

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 engines outputs, car routes, airline prices, and advertising content.

In this course, we explore the social, political, and cultural impact of our society’s reliance on massive (and often real-time) data analysis. We will discuss the concepts behind data collection, organization, analysis, visualization, 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.

The course will be structured around four broad concepts that inform data studies: defining data, algorithms, networks, and terms of service. This way, we will look at problems such as: how do we define data and where does it reside, what cultural values are encoded in the algorithms that present data to us, how does data travel unequally in the world, and how does big cultural data impact individuals and society.

Students will be expected to participate in class discussions, contribute to a weekly course blog, to present an oral presentation on a data case study (e.g., Cambridge Analytica, net neutrality, EU’s Right to Be Forgotten, Edward Snowden, Amazon Alexa, or the Memex), and to write a final research paper.

DATA 73200 - Interactive Data Visualization (#55616)

Hybrid, Room 3207
Tuesday, 6:30 - 8:30 PM, 3 credits, Prof. Mia Szarvas (msszarvas@gmail.com)
Online dates 2/28, 3/7, and 3/14. All other dates meet in person.  
DATA 73200 satisfies as a Data Visualization core course

Note: 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.

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 78000 - Special Topics: “Advanced Interactive Data Visualization Studio" (#55613)

Online
Tuesday, 6:30 - 8:30 PM, 3 credits, Prof. Ellie Frymire (ellie.frymire@gmail.com)
DATA 78000 “Advanced Data Vis Studio” satisfies as a Data Visualization core course 

Note: By permission of the instructor with the Registrar.

This course will offer students the opportunity to develop a professional level data visualization project of their choice. This course will be a supervised studio-style class, with the goal of helping students push forward their own design and development practice — as such, the course will support students through the process of concept development, design iteration, technical implementation, critique, and refinement. Students will pursue their individual interests while working in the context of a hands-on studio environment where they will interact and share ideas with peers.  The expectation is that students are motivated and prepared to develop their own project and goals.

A portion of the semester will also consist of a series of advanced technical workshops. The topics of these workshops will be informed by the tools students need in order to push their work forward. As such, this class will be both technically and conceptually challenging. It is strongly recommended that students complete ‘Interactive Data Visualization’ prior to taking this course, or have comparable experience with Javascript, HTML, and CSS. Ultimately, the goal is for each student to finish the semester with a professional level project they feel proud of.

DATA 71000 - Data Analysis Methods (#55615)

Online
Thursday, 4:15 - 6:15 PM, 3 credits, Prof. Liza Steele (lsteele@jjay.cuny.edu)
DATA 71000 satisfies as a Data Analysis core course  

The goal of this course is to provide students with an introduction to basic statistical techniques for analyzing data. Students will develop an understanding of concepts underlying modern statistics and statistical reasoning that will equip them with tools to analyze and visualize a variety of data types and data sources. We will first learn principles of descriptive statistics. Next, we will cover principles and techniques of inferential statistics. Students will explore various statistical measures and techniques for analyzing data, and practice applying this knowledge to real-world data problems. Practical topics include: descriptive and inferential statistics,  ordinary least squares regression, logistic regression, and exploratory data analysis.

DATA 71200 - Advanced Data Analysis (#55617)

Online
Thursday, 4:15 - 6:15 PM, 3 credits, Prof. Howard Everson (HEverson@gc.cuny.edu)
DATA 71200 satisfies as a Data Analysis core course

Note: Students should have a working knowledge of Statistics (through Regression methods) and at least a basic knowledge of Python to do the work required in this course. The course will be taught in Python, primarily using the scikit-learn library. Successful completion of DATA 71000 Data Analysis Methods will satisfy the prerequisite requirement.

This survey course is designed for students who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis, and who want to communicate their findings clearly to audiences of business groups, researchers, and other practitioners.  The course will introduce students to advanced data analytic methods and toolkits, including machine learning methods using the scikit learning library, that will equip them with the ability to perform analyses of complex data from business, industry, and the arts and sciences. The course will begin with an overview of regression analyses, including logistic regression, and continues with latent variable analysis and related exploratory data analytic methods. This course will cover both supervised methods (e.g., k-Nearest neighbors, naïve Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis, non-negative matrix factorization, and k-means clustering). The supervised methods will focus primarily on "classic" machine learning techniques where features are designed rather than learned, although we briefly look at recent deep learning models with neural networks The course will offer conceptual explanations of these statistical techniques and will provide opportunities for students to implement and practice these techniques using real data with the goal of helping students develop a sense of when machine learning is an appropriate tool versus other statistical modeling methods. Students will be encouraged to use Python and sci-kit learning tools to produce readable and sensible code that will enable others to replicate and extend their analyses.  

DATA 70500 - Working with Data: Fundamentals (52658)

Online
Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Tim Shortell (Shortell@brooklyn.cuny.edu)
DATA 70500 satisfies as a Data Analysis distribution core course 

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 71000 - Data analysis methods (60349)

Online
Thursdays, 4:15 - 6:15 PM, 3 Credits, Prof. Howard Everson (howard.everson@sri.com)
DATA 71000 satisfies as a Data Analysis distribution core course

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 (52656)

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

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 (52657)

Online
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 

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: “Software Design Lab” (52699)

In-person
Tuesday 6:30-8:30pm, 3 Credits, Prof. Omar Nema (omarwnema@gmail.com)
Cross-listed with DHUM 71000 

Software Design Lab is an introduction to software development as a practice and creative medium through a hands-on approach.

This course will guide students in developing a coding craft that is grounded in research, iterative design, and self-expression. Software Design Lab will introduce development methodologies through a hands-on approach: students will learn to code by gradually building their own interactive projects. Students will explore how software can be used as a creative medium, and how it can be integrated into their existing research or technical practices.

The course is run in a studio format, which means all students are expected to participate in the making, discussing, and critiquing work. Coursework will center around two web-based programming projects. Topics covered include: HTML/CSS/Javascript, interactivity, APIs, data visualization, and the web as a system. This course assumes no prior knowledge in software development.

DATA 73000 - Visualization and Design (52713)

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

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 70600 - Special Topics in Computational Fundamentals: JavaScript (52711)

Online
Thursday, 6:30 - 7:30 PM, 1 Credit, Prof. Stephen Zweibel (Szweibel@gc.cuny.edu)
Cross-listed with DHUM 70600
Note: This is a 1-credit, 1-hour lab course. Students can enroll in up to three 1-credit lab courses. 

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 71200 - Advanced Data Analysis Methods (12373)

Online, May 31st - June 22nd
4:15 - 6:15 PM, 3 Credits, Prof. Johanna Devaney (Johanna.Devaney@brooklyn.cuny.edu)
DATA 71200 satisfies as a Data Analysis distribution core course
This course will be entirely online, with synchronous and asynchronous class sessions. Synchronous class dates: 5/31, 6/1, 6/2, 6/6, 6/8, 6/13, 6/15, 6/21, 6/22

Note: Students should have at least a basic knowledge of Python to do the required work for the course.

This course will provide students will skills necessary to apply machine learning techniques to data, and interpret and communicate their results. They will also begin to develop intuitions about when machine learning is an appropriate tool versus other statistical methods. This course will cover both supervised methods (e.g., k-Nearest neighbors, naïve Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis, non-negative matrix factorization, and k-means clustering). The supervised methods will focus primarily on "classic" machine learning techniques where features are designed rather than learned, although we briefly look at recent deep learning models with neural networks. This is an applied machine learning class, which emphasizes the intuitions and know-how needed to get learning algorithms to work in practice, rather than mathematical derivations.

The course will be taught in Python, primarily using teh scikit-learn library. The course's main text will be the O'Reilly book "Introduction to Machine Learning with Python" by Sarah Guido and Andreas C. Müller, along with the book's corresponding Jupyter notebooks. We will also be referencing "The Elements of Statistical Learning" by Trevor Hastie, jerome H. Friedman, Robert and Tibshirani for examining some of the topics in more depth (this book is available for free form the first author's website: https://web.stanford.edu/~hastie/Papers/ESLII.pdf [web.stanford.edu]

DATA 70600 - Special Topics in Computational Fundamentals: Introduction to Front End Web Development (12377)

In-person, June 28th - August 4th
Tuesday & Thursday, 4:15 - 5:45 PM, 1 Credit, Prof. Will Field (wfield@gc.cuny.edu)
Course Dates: 6/28, 6/30, 7/5, 7/7, 7/19, 7/21, 7/26, 7/28, 8/2, 8/4
Cross-listed with DHUM 70600
Note: This is a 1-credit summer lab course. Students can enroll in up to three 1-credit lab courses. 

This class offers an introduction to website development using HTML, CSS, and JavaScript with a focus on JavaScript. 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 70600 - Special Topics in Computational Fundamentals: Mapmaking and Visual Storytelling (12378)

Online, July 5th - August 4th
Tuesday & Thursday, 6:30 - 8:00 PM, 1 Credit. Prof. Olivia Ildefonso (me@oliviaildefonso.com)
Course Dates: 7/5, 7/7, 7/12, 7/14, 7/19, 7/21, 7/26, 7/28, 8/2, 8/4
Cross-listed with DHUM 70600
Note: This is a 1-credit summer lab course. Students can enroll in up to three 1-credit lab courses.

In this class you’ll learn how to use ArcGIS Online and ESRI Story Maps to create engaging visual narratives. The course will begin with a lesson on the fundamentals of mapmaking, which includes a 101 on mapping concepts and an overview of mapping ethics. You will then spend the rest of the course working with a dataset from the 2020 U.S. Census to create an interactive, web-based map, an interactive dashboard, and a multimedia story map.

DATA 70600 - Special Topics in Computational Fundamentals: Python (In Person) #62035

Monday, 5:15 - 6:15 PM, 1 Credit, Prof. Rafael Davis Portela (rdportela@gmail.com)
Cross-listed with DHUM 70600

Note: This is a 1-credit, 1-hour lab course. Students may take a maximum of three 1-credit courses (a total of 3 credits) for elective credits.

This course is an introduction to Python, a general-purpose programming language with increasing popularity among academics and in the industry. In the course, we will cover installations, different coding tools, working with text editors, the basics of the command line and how to run scripts on it. We will learn the fundamentals of programming languages, such as variables, functions, types, conditionals, and loops. After that, we will introduce Python for Text Analysis with the NLTK library and for Data Analysis with the Pandas library. This course has a very hands-on approach, and students are expected to engage with exploratory analysis both in the class and out of the class. No previous programming knowledge is required.

DATA 73000 - Visualization and Design (In Person) #62036

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

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.

SOC 81900 - Spatial Data Analysis (In Person), #60743

Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Jeremy Porter (jporter@gc.cuny.edu)

Note: This course is a recommended elective.

Geographic Information Systems (GIS) has emerged as an essential tool for public health researchers and practitioners. The GIS for Public Health course will offer students an opportunity to gain skills in using GIS software to apply spatial analysis techniques to public health research questions. The laboratory section of the course will give students the opportunity for hands-on learning in how to use GIS systems to analyze data and produce maps and reports. These laboratory exercises will be designed to increasingly challenge the students to incorporate the analytic skills and techniques they have learned in other courses with the geospatial and spatial statistics techniques commonly used in GIS.

DATA 78000 - Special Topics: "Introduction to GIS: Methods and Applications" (Online) #62037

Tuesday, 6:30 - 8:30 PM, 3 Credits, Prof. Shipeng Sun (shipeng.sun@hunter.cuny.edu)
Cross-listed with DHUM 73700
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.

DATA 74000 - Data, Culture and Society (In Person) #62038

Wednesday, 4:15 - 6:15 PM, 3 Credits, Prof. Kevin Ferguson (kferguson@qc.cuny.edu)
DATA 74000 satisfies as a Data Studies distribution core course

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 engines outputs, car routes, airline prices, and advertising content.

In this course, we explore the social, political, and cultural impact of our society’s reliance on massive (and often real-time) data analysis. We will discuss the concepts behind data collection, organization, analysis, visualization, 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.

The course will be structured around four broad concepts that inform data studies: defining data, algorithms, networks, and terms of service. This way, we will look at problems such as: how do we define data and where does it reside, what cultural values are encoded in the algorithms that present data to us, how does data travel unequally in the world, and how does big cultural data impact individuals and society.

Students will be expected to participate in class discussions, contribute to a weekly course blog, to present an oral presentation on a data case study (e.g., Cambridge Analytica, net neutrality, EU’s Right to Be Forgotten, Edward Snowden, Amazon Alexa, or the Memex), and to write a final research paper.

DATA 73200 - Interactive Data Visualization (Hybrid) #62039

Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Rachel Daniell (rdaniell@gradcenter.cuny.edu)
DATA 73200 satisfies as a Data Visualization distribution core course

Note: This is a hybrid course. Virtual session dates: 2/2, 2/9, 2/16, 2/23, 3/2, 3/9, 3/16, 3/23. In-person session dates: 3/30, 4/6, 4/13, 4/27, 5/4, 5/11.

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 78000 - Special Topics: "Advanced Interactive Data Visualization Studio" (Online) #62040

Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Ellie Frymire (ellie.frymire@gmail.com)
DATA 78000 satisfies as a Data Visualization distribution core course

Note: By permision of instructor with the Registrar.

This course will offer students the opportunity to develop a professional level data visualization project of their choice. This course will be a supervised studio-style class, with the goal of helping students push forward their own design and development practice — as such, the course will support students through the process of concept development, design iteration, technical implementation, critique, and refinement. Students will pursue their individual interests while working in the context of a hands-on studio environment where they will interact and share ideas with peers.  The expectation is that students are motivated and prepared to develop their own project and goals.

A portion of the semester will also consist of a series of advanced technical workshops. The topics of these workshops will be informed by the tools students need in order to push their work forward. As such, this class will be both technically and conceptually challenging. It is strongly recommended that students complete ‘Interactive Data Visualization’ prior to taking this course, or have comparable experience with Javascript, HTML, and CSS. Ultimately, the goal is for each student to finish the semester with a professional level project they feel proud of.

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?

DATA 71200 - Advanced Data Analysis Methods (Online) #12471

6/1 - 6/24, 6:30 - 8:30 PM, 3 Credits, Prof. Johanna Devaney
(Johanna.Devaney@brooklyn.cuny.edu)
DATA 71200 satisfies as a Data Analysis distribution core course

Note: Course will be entirely online, with synchronous and asynchronous class sessions. Synchronous class session dates TBA. First class session: June 1st.

This course will provide students will skills necessary to apply machine learning techniques to data, and interpret and communicate their results. They will also begin to develop intuitions about when machine learning is an appropriate tool versus other statistical methods. This course will cover both supervised methods (e.g., k-Nearest neighbors, naïve Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis, non-negative matrix factorization, and k-means clustering). The supervised methods will focus primarily on "classic" machine learning techniques where features are designed rather than learned, although we briefly look at recent deep learning models with neural networks. This is an applied machine learning class, which emphasizes the intuitions and know-how needed to get learning algorithms to work in practice, rather than mathematical derivations.

The course will be taught in Python, primarily using teh scikit-learn library. The courses's main text will be the O'Reilly book "Introduction to Machine Learning with Python" by Sarah Guido and Andreas C. Müller, along with the book's corresponding Jupyter notebooks. We will also be referencing "The Elements of Statistical Learning" by Trevor Hastie, jerome H. Friedman, Robert and Tibshirani for examining some of the topics in more depth (this book is available for free form the first author's website: https://web.stanford.edu/~hastie/Papers/ESLII.pdf [web.stanford.edu]

DATA 73000 - Visualization and Design (Online) #12469

6/1 - 6/24, 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 core course

Note: Course will be entirely online, with synchronous and asynchronous class sessions. First class session: June 1st.

Synchronous class dates: 6/1, 6/3, 6/8, 6/10, 6/15, 6/17, 6/22, 6/24
Asynchronous class dates: 6/2, 6/7, 6/9, 6/14, 6/16, 6/21, 6/23

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.

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.

DATA 73000: Visualization and Design #58890

Professor Lev Manovich
Monday, 4:15 - 6:15 PM 3 Credits
Cross-listed with C SC 83060

Data visualization is increasingly important today in more and more fields. Its growing popularity in the early 21st century corresponds to important cultural and technological shifts in our societies – adoption of data-centric research methods in many new areas, the availability of  massive data sets, and use of interactive digital media and the web for dissemination of information and knowledge. Data visualization techniques allow people to use perception and cognition to see patterns in data, and form research hypotheses. During last 20 years data visualization has also become an important part of contemporary visual and data cultures, visual communication, interactive media and interface design.

In this course students learn the concepts and methods of data visualization. The key part of the class is learning and practicing outside. I will recommend online resources (tutorials and short online classes) suitable for students’ backgrounds and previous knowledge. To test what students learn, I will assign two practical homeworks and a final project. These assignments will be discussed and analyzed in class. 
In addition, the class covers the following topics:

1. Learning about data visualization field, becoming familiar with most well-known designers and data artists, classic visualization projects, relevant organizations and available software. 
2. Visualization can be understand as a part of a scientific paradigm for summarizing, analyzing and predicting data that also includes statistics, data science and AI. Accordingly, students will be introduced to selected concepts from these areas so they understand how visualization interacts with them.
3. Alternatively, visualization can be seen as a part of modern culture that includes languages and techniques of visual art, design, architecture, cinema, interactive art, and data art. We will devote some time to discussing these perspectives and connections. The students will be introduced to basic principles of modern design as they apply to visualization.
4. Another topic which we will also cover is the use of visualization in recently emerged fields devoted to analyzing big cultural data - digital humanities, computational social science, and cultural analytics.

DATA 73000: Visualization and Design #62253

Professor Michelle McSweeney
Monday, 6:30 - 8:30 PM
3 Credits

Data are 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. 

DATA 78000: Advanced Interactive Data Visualization Studio #64563

Professor Aucher Serr
Tuesday, 6:30 - 8:30 PM
3 Credits
Website

This course will offer students the opportunity to develop a professional level data visualization project of their choice. This course will be a supervised studio-style class, with the goal of helping students push forward their own design and development practice — as such, the course will support students through the process of concept development, design iteration, technical implementation, critique, and refinement. Students will pursue their individual interests while working in the context of a hands-on studio environment where they will interact and share ideas with peers.  The expectation is that students are motivated and prepared to develop their own project and goals.

A portion of the semester will also consist of a series of advanced technical workshops. The topics of these workshops will be informed by the tools students need in order to push their work forward. As such, this class will be both technically and conceptually challenging. It is strongly recommended that students complete ‘Interactive Data Visualization’ prior to taking this course, or have comparable experience with Javascript, HTML, and CSS. Ultimately, the goal is for each student to finish the semester with a professional level project they feel proud of. Note: By Permision of Instructor with the Registrar

DATA 78000: Introduction to GIS: Methods and Applications #63300

Professors Yuri Gorokhovich & Elia Machado
Tuesday, 6:30 - 8:30 PM
3 Credits
Cross-listed with DHUM 73700 (#63299) and EES 79903

Introduction to the fundamentals of Geographic Information Systems (GIS) including vector and raster data formats and applicable analytical techniques. Emphasis on spatial data representation, organization, analysis, and data integration including remote sensing.  Theoretical and technical concepts are reinforced through hands-on exercises illustrating GIS applications in hydrology, conservation biology, engineering, geology (topographic analysis), multicriteria-evaluation, and decision making

DATA 73200: Interactive Data Visualization #62268

Professor Ellie Frymire
Wednesday, 6:30 - 8:30 PM
3 Credits
Website

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 71000: Data Analysis Methods #64613

Professor Everson
Thursday, 4:15 - 6:15 PM
3 Credits
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 71200 - Advanced Data Analysis Methods #61133
Prof. Johanna Devaney (Johanna.Devaney@brooklyn.cuny.edu)

This course will provide students will skills necessary to apply machine learning techniques to data, and interpret and communicate their results. They will also begin to develop intuitions about when machine learning is an appropriate tool versus other statistical methods. This course will cover both supervised methods (e.g., k-Nearest neighbors, naïve Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis, non-negative matrix factorization, and k-means clustering). The supervised methods will focus primarily on "classic" machine learning techniques where features are designed rather than learned, although we briefly look at recent deep learning models with neural networks. This is an applied machine learning class, which emphasizes the intuitions and know-how needed to get learning algorithms to work in practice, rather than mathematical derivations.

The course will be taught in Python, primarily using teh scikit-learn library. The courses's main text will be the O'Reilly book "Introduction to Machine Learning with Python" by Sarah Guido and Andreas C. Müller, along with the book's corresponding Jupyter notebooks. We will also be referencing "The Elements of Statistical Learning" by Trevor Hastie, jerome H. Friedman, Robert and Tibshirani for examining some of the topics in more depth (this book is available for free form the first author's website: https://web.stanford.edu/~hastie/Papers/ESLII.pdf [web.stanford.edu]

DATA 73200 - Interactive Data Visualization #61138  (Note: two sections offered--same day and time)
Prof. Aucher Serr (aucher.serr@gmail.com)

DATA 73200 - Interactive Data Visualization #64845 (Note: two sections offered--same day and time)
Prof. Ellie Frymire (ellie.frymire@gmail.com)

Website

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 73500 - Working with Data #66137 (Note: there are two sections on Monday)
Prof. Timothy Shortell (dr.timothy@shortell.nyc)

DATA 73500 - Working with Data #61122 (Note: there are two sections on Monday)
Prof. Timothy Shortell (dr.timothy@shortell.nyc)

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 focussed 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 emergene of a number of new research fields in the end of the 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 begin with a broader examination of data 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. We'll focus mainly on analysis of text in our coding work; this is the best place to begin to understand the choices we make as researchers and analysts in applied settings. 

Students will learn the most fundamental concepts and skills of data analysis, required before they can use more advanced analysis techniques, and also do data visualization. While focusing on fundamentals, the course also introduces students to new ideas for data analysis, new types and sources of data, and recently emerged fields that are taking advantage of these sources, increasing computer power for data processing and new open source comprehensive data analysis programming environments. After taking this course, students will be able to: 

* have a general understanding of how to use quantitative data to research topics in many fields; 
* understand both the benefits and limitations of using quantitative methods in research; 
* learn concepts and practical techniques for downloading data from various sources, cleaning data, managing and structruing datasets using tools such as Google Sheets and the R data analysis programming language. 
* students will acquire working knowledge of a language such as R or Python, including importing and exporting data in different formats, data management, selecting parts of a dataset using various conditions, combining data sets, and creating basic graphs. 
 
DATA 74000 - Data, Culture, and Society #61128
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 74200 - Media Theory and History #61130
Prof. Lev Manovich (lmanovich@gc.cuny.edu)

The topics course is designed to introduce students to many influential ideas and works by key modern and contemporary thinkers about media and technology. Because historically these ideas were developed in relation to particular technologies and media that came into prominence in different periods, we will also explore aspects of media history including photography, film, radio, television, Internet, social media, artificial intelligence, big data and data art. Some of the discussions will use as starting points Manovich's own selected articles and chapters from his books The Language of New Media, Software Takes Command, Instagram and Contemporary Image, and Cultural Analytics (forthcoming). all texts used in the class are freely available online.

DATA 78000 - Geospatial Humanities #61134
Prof. Jonathan Peters (jonathan.peters@csi.cuny.edu)
Cross-listed with DHUM 73700

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 in a fun and engaging way. The course examines the storage, processing, compilation, and symbolization of spatial data; basic spatial analysis and spatial statistics; and the 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. Students will develop original maps using various forms of data collection, analysis and historical resources.
 
The overarching objective of this course is to familiarize students with GIS and spatial analysis tools and techniques used in professional and scholarly fields. By the conclusion of this course, students will be able to:
* gather and manipulate geospatial data;
* interact with geospatial data stored in a database;
* interact with geospatial data stored in hierarchical data formats;
* explore historical geospatial data resources and understand variations in data reporting based upon time period and location;
* collect geospatial data in field using GPS technology and map as needed;
* use cartographic theory to design effective graphical representations of geospatial data;
* use cartographic theory to interpret, analyze, and critique graphical representations of spatial phenomena;
* and create both static and interactive maps containing different representations of geospatial information. 

Texts:
Mastering ArcGIS by Maribeth H. Price – Seventh Edition.  ISBN-13: 978-0078095146 $78.25 MSRP
Getting to Know ArcGIS Desktop Second Edition, for ArcGIS 10 Edition by Tim Ormsby, Eileen J. Napoleon, Robert Burke, Carolyn Groessl ISBN-13: 978-1589482609 $25.00 MSRP.
Lost City of the Monkey God by Douglas Preston ISBN 9781455569410 – selected chapters as noted
Topics / Academic Papers as noted

DATA 73300 - Visualization and Design: Fundamentals #62520

Professor Michelle McSweeney

Data are 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.

DATA 73300 - Visualization and Design: Fundamentals #62520

Prof. Michelle McSweeney

Data are 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.

DATA 71000 - Data Analysis Methods #59983

Professor Mariya Bessonov

The goal of this course is to provide students with an introduction to basic statistical techniques for analyzing data. Students will develop an understanding of concepts underlying modern statistics and statistical reasoning that will equip them with tools to analyze variety of data types and data sources and also visualize it. We will first learn principles of descriptive statistics. Next, we will cover principles and techniques of inferential statistics, and design of experiments. Students will explore various statistical measures and techniques for analyzing data, and practice applying this knowledge to real-world data problems. Practical topics include: descriptive and inferential statistics, sampling, experimental design, statistical models, parametric and non-parametric tests, ordinary least squares regression, logistic regression, and explorative data analysis.

DHUM 73700 - Geospatial Humanities #59981

Professor Jeremy Porter

This course aims to familiarize students with GIS and spatial analysis tools and techniques used in the visualization, management, analysis, and presentation of geo-spatial data. The course will be a hand's on applied course in which students will learn to work with publicly available geo-spatial data in open-source software packages, including but not limited too: R, Python, QGIS, and CartoDB. Topics covered include, Data Acquisition, Geo-Processing, Data Visualization, Cartography, Spatial Statistics, and Web-Mapping.

DATA 73000 - Visualization and Design: Fundamentals

Professor Lev Manovich

Data visualization is increasingly important today in more and more fields. Its growing popularity in the early 21st century corresponds to important cultural and technological shifts in our societies – adoption of data-centric research methods in many new areas, the availability of  massive data sets, and use of interactive digital media and the web for dissemination of information and knowledge. Data visualization techniques allow people to use perception and cognition to see patterns in data, and form research hypotheses. During last 20 years data visualization has also become an important part of contemporary visual and data cultures, entering the worlds of art, visual communication, interactives and interface design.

In this course students learn the concepts and methods of data visualization. They practice these methods by completing four practical assignments and a final project. These assignments  will be discussed and analyzed in class.  In addition, the class covers the following four topics:

1) Learning about data visualization field, becoming familiar with most well-known designers and data artists, classic visualization projects, relevant organizations and available software.

2) Visualization can be understand as a part of a scientific paradigm for summarizing, analyzing and predicting data that also includes statistics, data science and AI. Accordingly, students will be introduced to selected concepts from these areas so they understand how data visualization interacts with these fields.

3) Alternatively, visualization can be seen as a part of modern culture that includes languages and techniques of visual art, design, architecture, cinema, interactive art, and data art. We will devote some time to considering these perspectives and links.

4) Another topic which we will also cover is the use of visualization in recently emerged fields devoted to analyzing big cultural data - digital humanities, computational social science, and cultural analytics.

DATA 73500 - Working with Data: Fundamentals

Professor Delaram Kahrobaei

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 lead to 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. 
 

DATA 74000 - Data, Culture, and Society

Professor Kevin Ferguson

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 engines outputs, car routes, airline prices, and advertising content.

In this course, we explore the social, political, and cultural impact of our society’s reliance on massive (and often real-time) data analysis. We will discuss the concepts behind data collection, organization, analysis, visualization, 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. 

The course will be structured around four broad concepts that inform data studies: defining data, algorithms, networks, and terms of service. This way, we will look at problems such as: how do we define data and where does it reside, what cultural values are encoded in the algorithms that present data to us, how does data travel unequally in the world, and how does big cultural data impact individuals and society. 

Students will be expected to participate in class discussions, contribute to a weekly course blog, to present an oral presentation on a data case study (e.g., Cambridge Analytica, net neutrality, EU’s Right to Be Forgotten, Edward Snowden, Amazon Alexa, or the Memex), and to write a final reseach paper.

DATA 73000 - VIsualization and Design: Fundamentals

Professors Erin Daugherty and Michelle McSweeney

As employers in every sector continue to search for candidates that can turn their data into actionable information, this course is designed to demystify data analysis by approaching it visually. Using Tableau Software, we will build a series of interactive visualizations that combine data and logic with storytelling and design. Over the course of four weeks, we will dive into cleaning and structuring unruly data sets, identifying which chart types work best for different types of data, and unpacking the tactics behind effective visual communication. Our data sets will be geared towards humanities and social science research, and Tableau’s drag-and-drop interface will not require coding. Regardless of your academic concentration, you will walk away from this class with a portfolio of four dynamic dashboards and a new interdisciplinary skill set ready to leverage in your academic and professional work.