General Information

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)

Laboratories:

• 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.

1. Samrina Sattar Tuesdays, Wednesdays 8 - 10 pm E-mail: ssattar at bmc
2. Check the CS110 webpage for more times when a TA is available

These are the hours when the Lab will not be available:

• Thursdays 11:30-1 pm

Syllabus: PDF version of the basic course information contained on this website.

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Texts & Software

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Important Dates

January 22: First lecture
March 6: Exam 1
May 1: Last lecture/Exam 2

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Assignments and Visualizations

Extra Credit 2: Complete Chapter 5, Exercise 3a by last day of written work.

Current Assignment

• 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

Past Assignments

Visualizations and other Web Explorations

1. Biografx Scientific and Medical Images
2. Apophysis: Fractal Generator
3. 2007 Visualization Challege
4. The National Atlas
5. IUCN Red List of Threatened Species
6. Alogirthmic Botany
7. A Beautiful WWW
8. New York Times Visualizations (Link 1 Link 2)
9. Information Aesthics

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Lectures

• Week 1 (January 22, 24)
  
  January 22: Class Organization
  
  January 24: Introduction to Visualization: Visual Perception and Principles
  

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• Week 2 (January 29, 31)
  
  January 29: Introduction to Computing
  
  January 31: Visualization through Computing: Introduction to Programming, Python and IDLE in PK 231
  

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• Week 3 (February 5, 7)
  
  February 5 : Data Perception
  
  February 7 : Modules and Functions
  

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• Week 4 (February 12, 14)
  
  February 12: Names and Values
  
  February 14: Programs and Problem Solving
  

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• Week 5 (February 19, 21)
  
  February 19 : Building a Visualization
  
  February 21: Univariate Visualization
  

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• Week 6 (February 26, 28)
  
  February 26: Sequences
  
  February 28: Chart Junk
  

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• Week 7 (March 4, 6)
  
  March 4: Exam Review
  
  March 6: Mid-Term (Exam 1) - In Class
  

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• Week 8 (March 11, 13)
  
  No classes. Spring Break!
  

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• Week 9 (March 18, 20)
  
  March 18: Class Cancelled
  
  March 20: Bivariate Visualization
  

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• Week 10 (March 25, 27)
  
  March 25: Graphical Excellence; Circumstancial Evidence
  
  March 27: Conditions
  
  

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• Week 11 (April 1, 3)
  
  April 1: Class Cancelled
  
  April 3: Making Decisions; Drawing a Histogram

• Week 12 (April 8, 10)
  
  April 8: The Math Library and String Processing
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  April 10: More String Processing
  

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• Week 13 (April 15, 17)
  
  April 15: File Input/Output
  
  April 17: Recursion/Multivariate Visualization Intro
  

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• Week 14 (April 22, 24)
  
  April 22: Multifunctional Graphical Elements
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  April 24: Color Maps
  

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• Week 15 (April 29, May 1)
  
  April 29: Effects without Causes
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  May 1: Exam Review Final (Exam 2) - Take Home

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Grading

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%
Project: 20%
Journal: 10%
Total: 100%

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Links

The graphics.py file

HTML documentation of graphics.py

A database of color names

The zlib1.dll file

The backend_tkagg.py file

Booksellers:

Amazon.com

Barnes and Noble

Powells

Alibris

Abebooks

Created on January 18, 2008.