slide 1 - cse 564 intro
TRANSCRIPT
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Example: Datasets obtained by 3D volumetric scans (CT, MRI)
what are some questions you might have?
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Example: Datasets obtained by 3D volumetric scans (CT, MRI)
what are some questions you might have?
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Example: Datasets obtained by 3D Simulations
what are some questions you might have?
one question might be:
how do planets form by ways of gravitational instabilities? hypothesis: matter clumps together and attracts more matter
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Example: Data obtained by observation-supported simulations
what are some questions you might have?
one question might be:
how did hurricane Katrina evolve?
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Example: Dow Jones Industrial Average
what are some questions you might have?
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Example: Political poll data
what are some questions you might have?
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Example: LinkedIn professional network
what are some questions you might have?
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Example: How do people call “soft drinks” in the US?
depends where you are…
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Example: use of time before a 15-page essay is due for class
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Example: Percent chance that a bar will reach the top of a box
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The salient features of a car:
miles per gallon (MPG)
top speed
acceleration
number of cylinders
horsepower weight
year
country origin
brand
number of seats
number of doors
reliability (# of breakdowns) and so on...
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That is where the challenge begins….
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Dr. John Snow’s LondonCholera Map (1854)
data collection
data assimilation
statistical testing
visualization
computationalanalysis (brain)
domain knowledge
Very early example of
visual analytics
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Make decisions based on data
not purely on intuition andlong business experience
use a combination of these
Visual
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The U.S. will need 140,000-190,000 predictive analysts and 1.5million managers/analysts by 2018
McKinsey Global Institute’s June 2011
Why do we need many more knowledgeable managers? because data scientists may work for more than one group
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HumanComputer
Visual Interface
Data
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HumanComputer
computing hardware
algorithms
Visual Interface
Data
manage
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HumanComputer
computing hardware
algorithms
pattern recognition
creative thought
Visual Interface
Data
manage
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HumanComputer
computing hardware
algorithms
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
manage
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
manage
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
manage
formalized insight
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
update
manage
visualize
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
interact
manage
learn
apply/update
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
update
manage
visualize
apply/update
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
Data
interactupdate
manage
learn visualize
apply/update apply/update
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HumanComputer
computing hardware
formal model
algorithms
formatted knowledge
pattern recognition
mental model
creative thought
abstracted knowledge
Visual Interface
visual communication
Data
interactupdate
manage
learn visualize
apply/update apply/update
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Count the number of black dots
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Which circle in the middle is bigger?
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So the human visual system (HSV) is not perfect, but it’sextremely powerful
Vision is an integral part of life
Vision is the gateway to higher-level regions of the brain
Exploit this fast and powerful processor for
complex data analyses, creative tasks, communicating ideas
The science of visualization and visual analytics
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Required
Optional
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Lecture Topic Projects
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Lecture Topic Projects
1 Intro, schedule, and logistics
2 Applications of visual analytics and basic tasks
3 Introduction to D3, basic vis techniques for non-spatial data Project #1 out
4 Visual perception and cognition
5 Visual design and aesthetics
6 Data types, notion of similarity and distance7 Data preparation and reduction Project #1 due
8 Introduction to R, statistics foundations Project #2 out
9 Data mining techniques: clusters, text, patterns, classifiers
10 Data mining techniques: clusters, text, patterns, classifiers
11 Computer graphics and volume rendering
12 Techniques to visualize spatial (3D) data Project #2 due
13 Scientific and medical visualization Project #3 out
14 Scientific and medical visualization
15 Midterm #1
16 High-dimensional data, dimensionality reduction Project #3 due
17 Big data: data reduction, summarization
18 Correlation and causal modeling
19 Principles of interaction
20 Visual analytics and the visual sense making process Final project proposal due
21 Evaluation and user studies22 Visualization of time-varying and time-series data
23 Visualization of streaming data
24 Visualization of graph data Final Project preliminary report due
25 Visualization of text data
26 Midterm #2
27 Data journalism
Final project presentations Final Project slides and final report due
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Projects (3): 10% each
Midterm (2) : 20% each
Final Project: 30%
proposal: 10%
prelim report: 10%
final report and presentation: 10%
Participation
not graded, but I hope you will attend regularly and participate
actively
For late submission policy see website