ver: 1.0

date: 2018-06-18

Instructor

Adj. Prof. Vladimir Skvortsov

Phone

032-626-1212

E-mail

vlad at sunykorea.ac.kr or vladimir.skvortsov at stonybrook.edu (be sure to include ”[CSE564]” with no spaces, in the subject line of any e-mail message you send to me)

Office

Building B, room 409

Office Hours

Mo, We 1:00-1:45 PM or by appointment. Office hours are only held when classes are in session.

Calendar

See the course syllabus for a list of textbooks, grading, a tentative schedule of topics, as well as the deadlines for all assignments

Lectures

See the academic calendar

Objectives

This course aims to introduce to both the foundations and applications of visualization and visual analytics, for the purpose of understanding complex data in science, medicine, business, finance, and many others. It will begin with the basics - visual perception, cognition, human-computer interaction, the sense-making process, data mining, computer graphics, and information visualization. It will then move to discuss how these elementary techniques are coupled into an effective visual analytics pipeline that allows humans to interactively think with data and gain insight.

Course Learning Outcomes:

  • An ability to transform spatial and non-spatial data from science, medicine, commerce, etc. into interactive visual representations

  • An understanding of the perceptual and cognitive reasoning processes that occur in humans when exploring visual artifacts derived from data to gain insight into the underlying phenomena

  • Working knowledge of principles and methods in human-computer interaction, data mining, computer graphics, and information visualization as applied to visual sense-making and analytics

  • Practical experience with a number of popular public-domain data analysis and visualization packages and libraries

Structure

  • Two weekly sessions (each 75 minutes)

    • 1st session: lecture, practical exposition, discussion

    • 2nd session: lecture, practical

Textbooks

  1. [book-RS14-3e] Information Visualization: An Introduction by Robert Spence, 3nd Ed., Springer, 2014.

  2. [book-WGK10] Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew Ward, Georges Grinstein, and Daniel Keim, published by A K Peters, Ltd., 2010. ISBN-13: 978-1-56881-473-5.

  3. [book-TBG13] Pro Data Visualization using R and JavaScript by Tom Barker, Apress, 2013. ISBN-13: 978-1-43025-806-3.

  4. [book-CA15] "Data Mining: The Textbook" by Charu Aggarwal, Springer, 2015.

  5. "Now You See It: Simple Visualization Techniques for Quantitative Analysis" by Stephen Few, Analytics Press, 2009.

  6. "Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking" by F. Provost and T. Faucett, O’Reilly Media, 2013.

Content

- Introduction
- Issues
- Representation
- Presentation
- Interaction
- Design
- Case Studies

Schedule

Current date:

First day of semester:

Last day of semester:

A number of days during semester:

Last day of classes: (Final exams start)

Weekdays of class:

The days of studies from to .

The table lists the sections we will cover in each lecture. Revisions may be made during the semester. It lists the required reading in Notes, and it is important to do the reading before the scheduled class. Lectures falling on Holidays when no classes are held will be made-up in subsequent lectures.

Assignments

  • Five homework assignments (HW1-HW5)

  • Midterm Exam (ME) - This will be a written test

  • Final Project (FP)

Exams/Assignments schedule: TBA

Grading

Course grades will be based on a combination of:

  • HW - consists of 5 homework assignments, 7% each (35%)

  • ME - Midterm exam (30%)

  • FP - Final Project (35%)

Each assignment contributes points to a student’s final grade (there are 100 points total). The total # of points earned at the end of the semester will determine the student’s final letter grade, based on the thresholds below:

F

D

D+

C-

C

0-59

60-65

66-70

71-73

74-77

C+

B-

B

B+

A-

A

78-80

81-83

84-87

88-90

91-93

94+

Example: 90.94 points would award you a B+ grade but 90.95 rounds to 91 and would award you an A- grade.

Course grade calulator:

Enter 3 tab separated values (
HWHomeworks score
MEMidterm score
FPFinal Project score
) or replace the sample values

Total (points→letter):

→

Tools

R → https://cloud.r-project.org/

RStudio → https://www.rstudio.com/

br

RStudio

Shiny

R Packages

RStudio includes a code editor, debugging & visualization tools

Shiny helps you make interactive web applications for visualizing data

Developers created many packages to expand the features of R