2022w



Instructor
TA


M259 Visualizing Information (4 units)



Prof. George Legrady
Weihao Qiu, Yixuan Li

Lecture/lab: Tues-Thurs 3:30pm-5:20pm Ellings Studio 2611
Office Hours: by appointment


Course Content Copyright
Course materials are protected by US Copyright laws and by University policy. Contents of this course may not be reproduced, distributed, or displayed without my express prior written consent.

Course Description

M259 is a project-based course focused on techniques of information retrieval and algorithmic-based visualization. Course concentration on fundamentals of data visualization and design, with an emphasis on data query, data analysis and processing, and visualization in 3D interactive spatial visualization.

Data mining, Knowledge Discovery, Culture Analytics through MySQL

Goals: Discover unexpected, interesting patterns in a large dataset
Data Source: 110 million datasets of library check-outs/check-ins, Seattle Public Library source

3D Interactive Visualization
Goals: Design in 3D, interactive space, implement algorithms in java-based Processing
Data Source: 98 million datasets of library check-outs/check-ins, Seattle Public Library source

Student Defined Project
Goals: Student defined project building on skills acquired through previous assignments
Data Source: Student determined

Knowledge acquired through the course:
1) Learn to explore and retrieve significant data from a dataset with MySQL
2) Develop skills in the fundamentals of visual language expressed through programming
3) Visualize abstract data to reveal patterns and relationships
4) Normalize data to enhance legibility and coherence
5) Apply algorithms by which to organize data
6) Implement interactivity within 3D volumetric visualization
7) Correlate various data sources through JSON and APIs


Seattle Library Resources
SPL Library Search | Library Branches | Monthly Aggregates  | Kaggle

Dewey Decimal Classification
Dewey Classification | Dewey_Sections_CSV | Top 20 Dewey View

Data Description
SPL Metadata | SPL ItemTypesCollection InventoryClassification Anomalies

Course 
Questionnaire | Agreement | Access

Student Resources
Student Forum | Last Year's Syllabus | Previous Student Visualizations

Software Resources
MySQL Workbench | MySQL Tutorial | W3_Schools | Processing (reference)

PROJECT 1
KNOWLEDGE DISCOVERY, DATA ANALYTICS, FREQUENCY MAPPING

[wk 1].....Lecture 01.04

Lab 01.06
Course Overview | SPL_Data_Intro

MySQL Examples | StudentProjects | Various MySQL Queries | Additional Queries

[wk 2]..................01.11

01.13
MySQL Assignment | Prediction | ItemNumber | Distribution Pattern | Variance | Feminism

MySQL Student Work Review

PROJECT 2
3D SPATIAL & INTERACTION & CHANGE OVER TIME

[wk 3].....Lecture 01.18

Lecture/Lab 01.20

Visual Fundamentals | 2D Graph (intro to Processing)

3D Starter demo | PeasyCam | csv_upload | 3D Basic


[wk 4]..................01.25

01.27

3D Assignment | 3D Examples | 3D_InfoGraph (HSB, pers, log() | | 3D_labeling

Student Concept Development | Control P5 | 3D_Treemap

[wk 5]..................02.01

02.03

Lab & Individual Meetings

Student Work-in-Progress


[wk 6].....Lecture 02.08


02.10

Algorithms: Kohonen Wiki [demo] [original] | JSON | demo] | Co-Occurrence [code] [Wiki]
Visualize: Convex Hull Github [Wiki] [LuLiu] | 3D_automated_trajectory

Student 3D Project Presentation


PROJECT 3
STUDENT-DEFINED VISUALIZATION

[wk 7]..................02.15
Data Resource

Lab 02.17

Final Project Assignment | Dynamic Project Review
Tempoo | NOAA | Bren Data |

Lab and Individual Meetings (to discuss final projects)         


[wk 8].....Lecture 02.22

02.24
Lab and Individual Meetings

Final Project Work-in-Progress

[wk 9]..................03.01

03.03

Lab and Individual Meetings

Lab & Individual Meetings
| Webpage template

[wk 10]................03.08

03.10
Final Student Presentations

Final Student Presentations & Documentations Due

Grading Completion of projects 60%
Attendance, Research, Participation and Literature Review 40%

The course is designed to accommodate a broad range of expertises. All students will be expected to perform at the level of their expertise but programming experience is necessary.