Bryn Mawr College
CS 120: Visualizing Information
Dr. Emily Greenfest-Allen
Instructor: Emily Allen, PK 246l, x6503
E-Mail: egallen at brynmawr dot edu
Lecture Hours: Tuesdays & Thursdays , 2:30 p.m. to 4:00.m.
Room: PK 259
Lab Hours: Monday and Wednesday 1-3 pm in Room 231 (additional lab hours will also available, see below)
- Computer Science Lab Room 231 (Science Building)
- You will also be able to use your own computer to do the labs for this course.
Lab Assistants: The following Lab assitants will be available during the week (names and schedules will be posted by the end of this week) for assistance on lab assignments.
- Samrina Sattar Tuesdays, Wednesdays 8 - 10 pm E-mail: ssattar at bmc
- Check the CS110 webpage for more times when a TA is available
These are the hours when the Lab will not be available:
Syllabus: PDF version of the basic course information contained on this website.
Texts & Software
Tufte, Edward R. 2001. The Visual Display of Quantitative Information. Graphics Press, LLC: Chesire, Connecticut.
Python Software + IDLE. This software is already installed in the Computer Science Lab.
Pylab Interface and the Matplotlib library. This software is already installed in the Computer Science Lab.
Installation Instructions for the PC (Required Files in Links section)
Installation Instructions for the MAC
(NOTE: if IDLE keeps crashing or freezing after producing figures, you may need to replace the installed backend_tkagg.py file with the one provided in the links section)
Tufte, Edward R. 2006. Beautiful Evidence. Graphics Press, LLC: Chesire Connecticut.
Zelle, John. 2004. Python Programming: an Introduction to Computer Science. Frankln, Beedle, & Associates, Wilsonville, Oregon.
Image copyright Kenneth Edwards
(from the 2004 Visualization Challenge)
January 22: First lecture
March 6: Exam 1
May 1: Last lecture/Exam 2
Assignments and Visualizations
Extra Credit 2: Complete Chapter 5, Exercise 3a by last day of written work.
- Due by 12 Noon Tuesday April 29: Color Map Exercise. Download necessary files here. E-mail or submit images through Blackboard.
- Due by 12 Noon Tuesday April 29: Pick either a really good or a really bad visualization to share with the class. E-mail me/submit a version on Balckboard. The links below (Visualizations and other Web Explorations might be a good place to start).
- Due Friday May 2 at 5 pm. Completed Histogram program. Submit .py file via blackboard. To test your program, I have two (much nicer) data. files. Daily average temperature recorded in Philadelphia in the year 1880 and in the year 2001. For extra credit, personalize your histogram program in one of the ways discussed in class.
- Chapter 6 histogram solution
Visualizations and other Web Explorations
- Biografx Scientific and Medical Images
- Apophysis: Fractal Generator
- 2007 Visualization Challege
- The National Atlas
- IUCN Red List of Threatened Species
- Alogirthmic Botany
- A Beautiful WWW
- New York Times Visualizations (Link 1 Link 2)
- Information Aesthics
- Week 1 (January 22, 24)
January 22: Class Organization
January 24: Introduction to Visualization: Visual Perception and Principles
- Week 2 (January 29, 31)
January 29: Introduction to Computing
January 31: Visualization through Computing: Introduction to Programming, Python and IDLE in PK 231
- Week 3 (February 5, 7)
February 5 : Data Perception
February 7 : Modules and Functions
- Week 4 (February 12, 14)
February 12: Names and Values
February 14: Programs and Problem Solving
- Week 5 (February 19, 21)
February 19 : Building a Visualization
February 21: Univariate Visualization
- Week 6 (February 26, 28)
February 26: Sequences
February 28: Chart Junk
- Week 7 (March 4, 6)
March 4: Exam Review
March 6: Mid-Term (Exam 1) - In Class
- Week 8 (March 11, 13)
No classes. Spring Break!
- Week 9 (March 18, 20)
March 18: Class Cancelled
March 20: Bivariate Visualization
- Week 10 (March 25, 27)
March 25: Graphical Excellence; Circumstancial Evidence
March 27: Conditions
- Week 11 (April 1, 3)
April 1: Class Cancelled
April 3: Making Decisions; Drawing a Histogram
- Week 15 (April 29, May 1)
April 29: Effects without Causes
May 1: Exam Review Final (Exam 2) - Take Home
All graded work will be graded on a point scale, with the total worth of the assignment proportional to its complexity and difficulty. At the end of the semester, final grades will be calculated as a weighted average of all grades according to the following weights:
Exam 1: 20%
Exam 2: 20%
Labs Exercises: 30%
Created on January 18, 2008.