程序代写代做代考 graph html Assignment #2 (Nov-9)

Assignment #2 (Nov-9)
– Work with challenge data
– Divide and conquer your data
– Submit individually (one unique submission per
student. No plagiarism!)
– Explore your data using – Simple statistics
– Simple charts
– Report observations (What do you see?)
– Formulate hypotheses (Why could that be?)

Assignment #2 (Nov-9)
– Describe data
– General type of data
– How large and complex
– Fields, links,
– What’s your challenge question
– Explore
– 4-5 exploratory graphs – Explanations
– Simple stats
– Reflect and hypothesis – Reflect on data
– 3-5 hypotheses

Python programming

Outline for today
What are visualizations and what do we need to learn about them?
1. Visualization 101
2. Visualization Literacy
3. Basic Charts
4. Visualization design process

Visual representation of data:

Pre-attentiveness: –
– Parallelity:

Anscombe’s Quartet

Statistics
– Numbers + math
– Comparison
– Small differences
– Answers questions
(hypothesis testing)
– Detail
– Single information
– …
Visualization
– Overview
– Creates questions
(hypothesis generation)
– Serendipity
– Multiple information
– …
Communication
– Interpretation
– Critiquing
– Contextualization – Presentation
– …

Sense making process
Data
Numbers, relations, records, text, analysis, …
Action
Decisions Emotions, Knowledge Insight

Sense making process
Data
Numbers, relations, records, text, analysis, …
Visualization
visual representation
Action
Decisions Emotions, Knowledge Insight

Sense making process
Data
Numbers, relations, records, text, analysis, …
Visualization
visual representation
Information
Insights, Facts
Action
Decisions Emotions, Knowledge Insight

Exploration

Exploration Insights

Exploration Insights
Explanation

Exploration
Data centered Domain experts
Generating Insights
Lab Setting
Insights
Explanation

Exploration
Data centered Domain experts
Generating Insights
Lab Setting
Insights
Explanation
Human centered Non-experts
Conveying messages
In-the-wild / diverse

Visualization Literacy
– confidently use
–
visual queries
– interpreting visual patterns

Visualization Literacy Reading:
– Correctly decode (simple & complex) visual representations
– Know pitfalls and deceptions
– Think critically ‘beyond’ and see ‘through’ the visualization
Design:
– Create efficient and effective visualizations
– Design efficient and effective visualizations
Explore:
– perform tasks: ask and answer questions
– Interact with with visualizations

What are the differences?

Exploratory Data Analysis
– Observe your data from many perspectives
– Observe as many facets as possible
– It’s called the Grand Tour
– Obtain as much observations as possible
– Generate insight and information
– Take notes
– Collaborate
– Communicate

Distributions

Descriptive Statistics with Numpy
(lat.: medius), middle (think: median => middle)
(think: mode => most)
Mean == average

https://www.wolfram.com/mathematica/new-in-8/statistical-visualization/specify-bin-sizes-for-histograms.html

Violin plots

Violin plot

Violin plots

Violin plots for comparison

Violin plots vs. box plots
http://www.stat.cmu.edu/~rnugent/PCMI2016/ papers/ViolinPlots.pdf

More plots!
Dot plot Error bars Box plots Violin plots

Confidence Intervals (error bars)

Error Bars in Line Charts

Alternatives
Bar plot
Mean +
CIs (Uncertainty)
Box plot
Median + IQR (spread)
Violin plot
Median + Distribution

Cheat sheets

https://visualizationcheatsheets.github.io/

Pie charts

Pie Charts
+ Few values
+ Huge differences
+ Easy communication + Order by size!

Alternatives to pie charts

Multi-dimensional data

Parallel Coordinates Plot
+ Scalable
+ Consice
+ Good overview
– Depending on ordering
– Can suffer from clutter
– Visual path following can be hard

Multi Dimensional Scaling (MDS)
+ Dimension reduction
+ Visualization in 2D or 3D
+ Visual clustering
– Information lost – Creates artifacts:
– false neighbors and – Tears

Which visualization?

1. Data

1. Data
Item

1. Data
Item
Attribute

1. Data
Value
Attribute
Item

Visual Marks

Visual Variables
Bertin, Jacques. “Semiology of Graphics: Diagrams.” Networks, Maps 10.00690805.1987 (1983): 10438353.

Visual Variables

Double Diamond
https://uxdesign.cc/how-to-solve-problems-applying-a-uxdesign-designthinking-hcd-or- any-design-process-from-scratch-v2-aa16e2dd550b

Design Thinking
Design thinking is a human-centered approach to creative problem solving.
¡ñ is about people
¡ð empathy, problems, context, problem
http://www.theagileelephant.com/what-is-design-thinking/ https://www.ted.com/talks/david_kelley_human_centered_design Rowe, Peter G. Design thinking. MIT press, 1987.

Design Thinking
Design thinking is a human-centered approach to creative problem solving.
¡ñ is about people
¡ð empathy, problems, context, problem
¡ñ Highly creative
¡ð ideas, discussion, iteration
http://www.theagileelephant.com/what-is-design-thinking/ https://www.ted.com/talks/david_kelley_human_centered_design Rowe, Peter G. Design thinking. MIT press, 1987.

Design Thinking
Design thinking is a human-centered approach to creative problem solving.
¡ñ is about people
¡ð empathy, problems, context, problem
¡ñ Highly creative
¡ð ideas, discussion, iteration
¡ñ hands-on
¡ð develop, prototype, test, try, …
¡ñ
http://www.theagileelephant.com/what-is-design-thinking/ https://www.ted.com/talks/david_kelley_human_centered_design Rowe, Peter G. Design thinking. MIT press, 1987.

Design Thinking
Design thinking is a human-centered approach to creative problem solving.
¡ñ is about people
¡ð empathy, problems, context, problem
¡ñ Highly creative
¡ð ideas, discussion, iteration
¡ñ hands-on
¡ð develop, prototype, test, try, …
¡ñ Show, don’t tell
http://www.theagileelephant.com/what-is-design-thinking/ https://www.ted.com/talks/david_kelley_human_centered_design Rowe, Peter G. Design thinking. MIT press, 1987.

Design Thinking
Design thinking is a human-centered approach to creative problem solving.
¡ñ is about people
¡ð empathy, problems, context, problem
¡ñ Highly creative
¡ð ideas, discussion, iteration
¡ñ hands-on
¡ð develop, prototype, test, try, …
¡ñ Show, don’t tell
¡ñ iterative
¡ð failure, progress, iterate, feedback,…
http://www.theagileelephant.com/what-is-design-thinking/ https://www.ted.com/talks/david_kelley_human_centered_design Rowe, Peter G. Design thinking. MIT press, 1987.

Wrap-up
1. Many forms of visualization
2. Visualization works through pre-attentiveness and parallel
processing
3. Visualization is a process
4. Visualization literacy is the ability to correctly understand and
use visual representations of data
5. Many basic chart types
6. Visual variables for design
7. Design thinking process

Related Posts