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Faculty Books and Projects



Please note that this schedule is tentative and subject to change.  


  Monday Tuesday Wednesday

Prof. Manovich

DATA 73000

Visualization and Design: Fundamentals



  Prof. Ferguson
DATA 74000
Data, Culture, and Society
Prof. Kahrobaei
DATA 73500
Working with Data: Fundamentals

FALL 2018

DATA 73000 - Visualization and Design: Fundamentals
Mondays, 4:15pm-6:15pm, 3 credits, Prof. Lev Manovich
Cross-listed with DHUM 730000 and CSC 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, 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
Wednesdays, 6:30 - 8:30 p.m., 3 credits, Prof. 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
 Tuesdays, 6:30 - 8:30 p.m., 3 credits, Prof. 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.