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Faculty Books and Projects

 
 
 

Courses

FALL 2020

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

DATA 73000
[58890]
Visualization and Design
Prof. Manovich

    DATA 71000
[64613]
Data Analysis Methods
Prof. Everson
6:30 - 8:30 PM DATA 73000
[62253]
Visualization and Design
Prof. McSweeney
DATA 78000
[64563]
Advanced Interactive Data Visualization Studio
Prof. Serr

DATA 78000
[63300]
Introduction to GIS: Methods and Applications
Profs. Gorokhovich & Machado
DATA 73200
[62268]
Interactive Data Visualization
Prof. Frymire
 


Note: All fall 2020 courses will be online.

DATA 73000 - Visualization and Design #58890
Monday, 4:15 - 6:15 PM, 3 Credits, Prof. Lev Manovich (lmanovich@gc.cuny.edu)
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 - Visualizattion and Design #62253
Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Michelle McSweeney (michelleamcsweeney@gmail.com)

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
Tuesday, 6:30 - 8:30 PM, 3 Credits, Prof. Aucher Serr (aucher.serr@gmail.com)
Website: two-n.com

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
Tuesday, 6:30 - 8:30 PM, 3 Credits, Profs. Yuri Gorokhovich (yuri.gorokhovich@lehman.cuny.edu) & Elia Machado (elia.machado@lehman.cuny.edu)
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
Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Ellie Frymire (ellie.frymire@gmail.com)
Website: Two-n.com

Interactive Data Visualization is one of the most important forms of communication today — allowing users to better engage with data, detect patterns, and quickly gain insight into complicated topics. This course will introduce students to the tools, skills, and concepts necessary for making state-of-the-art interactive data visualizations. Using web-based technologies including HTML, CSS, and D3.js, students will learn to create engaging and effective information displays, grounded in the science of visual perception and best practices in visual mapping and accessibility. Throughout the semester, students will work towards creating a portfolio of beautiful and analytically sound data visualizations, while also developing their own iterative design process.

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
Thursday, 4:15 - 6:15 PM, 3 Credits, Prof. 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.