Syllabus

Course Information

Professor: Mikael Vejdemo-Johansson
mvejdemojohansson@gc.cuny.edu

Course Meetings: Wednesdays, 2.00 - 4.00
In person, CUNY Graduate Center, room: 4433

Course Rationale

Today quantitative and symbolic data are easily collected in computer format, from databases, websites, smart devices, and anything that has interconnect capabilities. When such large amounts of data are put in spreadsheets or tabular reports, it becomes difficult to see the patterns, structure, trends, or relationships inherent in the data. Effective data visualization exposes these inherent relationships, consolidating and illustrating them in graphics.

Course Description

A visualization organizes data in a way that the structure and relationships in the data that may not be so easily understood becomes easily understood and interpreted with the visualization. Visualizations of a data set give the reader a narrative that tells the story of the data.

The purpose of data visualization is to convey information contained in data to clearly and efficiently communicate an accurate picture of what the data says through understandable and context appropriate visualizations.

To do a visualization can be just exploratory or entails using Machine Learning techniques that determine the structure of the data. The visualizations are then matched to the data structure.

The course will explore how principles of information graphics and design and how principles of visual perception, can be used with machine learning techniques to make effective data visualizations.

Each student will make a presentation of some principles of data visualizations or do a visualization project.

The course is open to PhD students in all programs. Non-computer science students will be paired with computer science students for the visualization project.

Topic List

The topic list may include but is not limited to:

Visualization Theory

Human visual perception, its limits, strengths and weaknesses
Color theory, color blindness
Grammar of Graphics
Interactivity

Visualization Techniques

Pie and donut charts
Histograms
Scatter plots
Heat maps
Matrix diagrams
Candlestick charts
Bubble charts
Graphs and networks
Alluvial diagrams
Dendrograms
Ring charts
Tree diagrams
Treemaps
Polar area diagram
Parallel coordinate displays
Time series
Line charts
Cartograms and choropleths
Dot distribution maps

Visualization Issues

Visualization Tools

R / ggplot2 / shiny
Python / matplotlib / seaborn / bokeh / plotnine
JS / d3.js
Tableau

Learning Goals

After this course, you will…

Be able to describe the key design guidelines and techniques used for the visual display of information.
Understand how to best use the capabilities of visual perception in a graphic display.
Understand the principles of interactive visualizations.
Understand how Machine Learning techniques can determine data structure and pattern.
Explore and critically evaluate a wide range of visualization techniques and applications.

Assessment

Every student will do a project involving a presentation of the project at the end of the course.

Every student will read and summarize a recent research article in Data Visualization in the form of a blog post.

Literature

We will primarily work with:

Tamara Munzner: Visualization Analysis & Design (ISBN 978-1466-50891-0)
Leland Wilkinson: The Grammar of Graphics, 2nd ed (ISBN 978-0387-24544-7)

For Wilkinson, the CUNY GC Library gives you free access to an ebook, and cheap (approx.. $25) access to an on-demand printed softcover copy.

In addition to these, research articles and parts of Edward Tufte’s books will be occasionally assigned for additional reading. Check the course schedule for details.

The library holds Munzner, Wilkinson, and the four Tufte books in a course reserve.

--- title: "Syllabus" format: html --- ## Course Information **Professor:** Mikael Vejdemo-Johansson mvejdemojohansson@gc.cuny.edu **Course Meetings:** Wednesdays, 2.00 - 4.00 In person, CUNY Graduate Center, room: 4433 ## Course Rationale Today quantitative and symbolic data are easily collected in computer format, from databases, websites, smart devices, and anything that has interconnect capabilities. When such large amounts of data are put in spreadsheets or tabular reports, it becomes difficult to see the patterns, structure, trends, or relationships inherent in the data. Effective data visualization exposes these inherent relationships, consolidating and illustrating them in graphics. ## Course Description A visualization organizes data in a way that the structure and relationships in the data that may not be so easily understood becomes easily understood and interpreted with the visualization. Visualizations of a data set give the reader a narrative that tells the story of the data. The purpose of data visualization is to convey information contained in data to clearly and efficiently communicate an accurate picture of what the data says through understandable and context appropriate visualizations. To do a visualization can be just exploratory or entails using Machine Learning techniques that determine the structure of the data. The visualizations are then matched to the data structure. The course will explore how principles of information graphics and design and how principles of visual perception, can be used with machine learning techniques to make effective data visualizations. Each student will make a presentation of some principles of data visualizations or do a visualization project. The course is open to PhD students in all programs. Non-computer science students will be paired with computer science students for the visualization project. ## Topic List The topic list may include but is not limited to: ### Visualization Theory * Human visual perception, its limits, strengths and weaknesses * Color theory, color blindness * Grammar of Graphics * Interactivity ### Visualization Techniques * Pie and donut charts * Histograms * Scatter plots * Heat maps * Matrix diagrams * Candlestick charts * Bubble charts * Graphs and networks * Alluvial diagrams * Dendrograms * Ring charts * Tree diagrams * Treemaps * Polar area diagram * Parallel coordinate displays * Time series * Line charts * Cartograms and choropleths * Dot distribution maps ### Visualization Issues ### Visualization Tools * R / ggplot2 / shiny * Python / matplotlib / seaborn / bokeh / plotnine * JS / d3.js * Tableau ## Learning Goals After this course, you will… * Be able to describe the key design guidelines and techniques used for the visual display of information. * Understand how to best use the capabilities of visual perception in a graphic display. * Understand the principles of interactive visualizations. * Understand how Machine Learning techniques can determine data structure and pattern. * Explore and critically evaluate a wide range of visualization techniques and applications. ## Assessment Every student will do a project involving a presentation of the project at the end of the course. Every student will read and summarize a recent research article in Data Visualization in the form of a blog post. ## Literature We will primarily work with: * Tamara Munzner: Visualization Analysis & Design (ISBN 978-1466-50891-0) * Leland Wilkinson: The Grammar of Graphics, 2nd ed (ISBN 978-0387-24544-7) For Wilkinson, the CUNY GC Library gives you free access to an ebook, and cheap (approx.. $25) access to an on-demand printed softcover copy. In addition to these, research articles and parts of Edward Tufte's books will be occasionally assigned for additional reading. Check the course schedule for details. The library holds Munzner, Wilkinson, and the four Tufte books in a course reserve.