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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