- Time & Location
- Tuesday 4pm-5:15pm, Informatics West 107 (Lecture)
Thursday 4pm-5:15pm, Informatics West 109 (Lab)
First meeting: Tuesday, Aug. 27th, 2013
- Instructor
- Yong-Yeol Ahn (YY)
yyahn@indiana.edu
Office: Informatics East Room 316
Phone: (812) 856 2920
Office hours: Wednesday 3pm-5pm, or by appointment
(email me or use MeetMe)
- AI
- Vikas Rao Pejaver
vpejaver@imail.iu.edu
Office hours: Tuesday 10am-11am (Lindley Hall LH 310), Thursday 5:15pm-6:15pm (Info West I 109)
- Textbook
- Matthew O. Ward, Georges Grinstein, and Daniel Kim, Interactive
Data Visualization: Foundations, Techniques, and Applications [Amazon]
- Scott Murray, Interactive Data Visualization for the Web [Amazon]
- Programming
- We will learn basics of Javascript and D3.js to create visualizations and play
with them.
- Prerequisites
- This course is open to graduate students as well as advanced
undergraduate students. There is no formal requirements, but it is
recommended to have programming background (I210 & I211 or
equivalent). Also I308: "Information Representation" is a recommended
class before taking this class. Working knowledge of Javascript and
scripting languages (e.g. Python) will be helpful. Contact the
instructor if you are uncertain about your background.
Description
From car dashboards to cutting-edge scientific papers, we extensively
use visual representation of data. Data visualization is becoming a
crucial skill for knowledge workers, particularly in the era of big
data, because visualization is critical to understand big, messy
data. In this course we will learn fundamentals of data visualization
and create data visualizations using various types of data.
Objectives
By the end of the course, you will be able to evaluate data
visualizations based on the understanding of the principles of
visualization, various types of data, and the strengths and limitations
of an array of visualization techniques. You will be able to interpret
complex datasets by applying appropriate visualization techniques.
Final assessement
For the final assessment, you will choose one of two options: design
& implementation or analysis. In the design & implementation
project, you will pick a real visualization problem and design &
implement a new visualization. You will write a final paper reporting
the background, implementation details, analysis and evaluation of
their solution to the problem. In the analysis project, you will choose
a dataset and analyze it through various visualization techniques. You
will write a final paper that provides the data description, analysis
and evaluation of used techniques.
Class policies
- All announcements will be sent via email. You are responsible for
reading each announcement in detail.
- You should read all the assigned readings prior to the
class.
- You have the responsibility of backing up all their data and
code. Today is International
Backup Awareness Day! Use a backup drive, dropbox, or whatever service you
find it useful. If you are comfortable with commandlines, I highly
recommend using a version
control system, especially with hosting services such as github (IU provides a firewalled github), bitbucket, etc.
- Please contact the instructor if you have a disability that
require some arrangements so that appropriate arrangements can be
made.
Academic integrity
The principles of academic honesty and ethics will be enforced. Any
cases of academic misconduct (cheating, fabrication, plagiarism, etc)
will be thoroughly investigated and immediately reported to the School
and the Dean of Students.
You should actively discuss with others, but you should write your own
report. Credit all the sources (discussion with other students, used
softwares, etc).
Grading policy
- Attendance & Class participation 20%
- Project: 40%
- Assignments: 40%
Deliverables
The deliverables are
Project proposal (Due: 10/8)
An one or two page document that contains
- Project title
- Team members
- Project description
- Relevant literature and prior work
Proposal presentation (10/8-10/15)
We will follow the
Ignite format.
You should have
20 slides and each slide will
auto-advance every
15 seconds. You should submit the
slides the day before the class. It should address
- Motivation (why is it interesting? why do you care?)
- Relevant existing work
- Potential contribution (why and how does your project differ from
prior work?)
- Game plan
Progress report (#1 Due: 10/29, #2 Due: 11/19)
Final report (Due: 12/17)
Final presentation (12/10, 12/12)
Again we will follow the
Ignite format.
but this time
10 minues for each team. You should
have
40 slides and each slide will auto-advance every
15 seconds. You should submit the slides by the night
of 4/23.
Resources
Links
Softwares, libraries, and data
General
Network visualization
Geo-visualization
Books
See YYiki:
Information visualization books