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## Data Visualization

by: Elizabeth Gill

26

0

3

# Data Visualization JRNL 4970

Elizabeth Gill
AU
GPA 2.8

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notes from week 1/26
COURSE
Data Visualization
PROF.
Michael J. Koliska
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
data
KARMA
25 ?

## Popular in Journalism and Mass Communications

This 3 page Class Notes was uploaded by Elizabeth Gill on Monday February 8, 2016. The Class Notes belongs to JRNL 4970 at Auburn University taught by Michael J. Koliska in Fall 2015. Since its upload, it has received 26 views. For similar materials see Data Visualization in Journalism and Mass Communications at Auburn University.

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Date Created: 02/08/16
Types of Data - Nominal - Ordinal - Categorical - Interval - Ratio Which data types/scales are categorical data and why? - Nominal - Ordinal Categorical - Colors - Types of Candies - Shirts - Gender - Countries - Animals Ordinal - Some sort of ranking - Put in order Which data types/scales are numerical data and why? Interval - No zero Ratio - Absolute zero What type of data is the most ideal to have? The Truth about Data Mean, median, and mode and point of reference Data - derives from latin dare= to give - plural of datum= something given - 17 /18 century became data Something Given… - No intrinsic truth - But truth can be found in data - Context needed to be more accurate Context for Data - who and what is the intent - when - what was measured - errors and uncertainty - filtering of data 1. Counting or totaling something 2. Proportion 3. Internal comparison 4. Change over time 5. “league tables” –compare groups 6. Analysis by categories 7. Association What did we learn? - context matters - be alert Mean = average Median = middle Mode = number or value that occurs most Range = Maximum - Minimum 1/19/16 Class Overview - use it to evaluate performances: sports - quantification of the world Consider all human history. How much information (data) in the world today was collected in 2012? (percent) - more than 90% of the data was collected in the past two years Big Data So what is big data? - data comes from everywhere: posts to social media, etc.. The problem with numbers: - not always easy to comprehend - can lead to cognitive bias - Framing (loss and gain) - And to discover the formerly unexpected Designing a data visual is like writing a story - you cant include your information - you have to selectively edit and order your information into a visual format When “visualizing” data, consider how the info matters to people and how they process the info

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